Inference approach 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0ff4bc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-13T16:20:29.523101Z",
     "iopub.status.busy": "2022-09-13T16:20:29.522350Z",
     "iopub.status.idle": "2022-09-13T16:22:32.122916Z",
     "shell.execute_reply": "2022-09-13T16:22:32.121604Z"
    },
    "papermill": {
     "duration": 122.608036,
     "end_time": "2022-09-13T16:22:32.125647",
     "exception": false,
     "start_time": "2022-09-13T16:20:29.517611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /kaggle/input/kerasapplications/Keras_Applications-1.0.8-py3-none-any.whl\r\n",
      "Requirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from Keras-Applications==1.0.8) (3.7.0)\r\n",
      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.7/site-packages (from Keras-Applications==1.0.8) (1.21.6)\r\n",
      "Installing collected packages: Keras-Applications\r\n",
      "Successfully installed Keras-Applications-1.0.8\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\r\n",
      "\u001b[0mProcessing /kaggle/input/qubvel/efficientnet-1.0.0-py3-none-any.whl\r\n",
      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /opt/conda/lib/python3.7/site-packages (from efficientnet==1.0.0) (1.0.8)\r\n",
      "Requirement already satisfied: scikit-image in /opt/conda/lib/python3.7/site-packages (from efficientnet==1.0.0) (0.19.3)\r\n",
      "Requirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet==1.0.0) (3.7.0)\r\n",
      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet==1.0.0) (1.21.6)\r\n",
      "Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (2.19.3)\r\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (1.3.0)\r\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (9.1.1)\r\n",
      "Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (2.5)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (21.3)\r\n",
      "Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (1.7.3)\r\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0) (2021.11.2)\r\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.7/site-packages (from networkx>=2.2->scikit-image->efficientnet==1.0.0) (5.1.1)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->scikit-image->efficientnet==1.0.0) (3.0.9)\r\n",
      "Installing collected packages: efficientnet\r\n",
      "Successfully installed efficientnet-1.0.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\r\n",
      "\u001b[0mProcessing /kaggle/input/qubvel/image_classifiers-1.0.0-py3-none-any.whl\r\n",
      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /opt/conda/lib/python3.7/site-packages (from image-classifiers==1.0.0) (1.0.8)\r\n",
      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->image-classifiers==1.0.0) (1.21.6)\r\n",
      "Requirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->image-classifiers==1.0.0) (3.7.0)\r\n",
      "Installing collected packages: image-classifiers\r\n",
      "Successfully installed image-classifiers-1.0.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\r\n",
      "\u001b[0mProcessing /kaggle/input/qubvel/segmentation_models-1.0.0-py3-none-any.whl\r\n",
      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /opt/conda/lib/python3.7/site-packages (from segmentation-models==1.0.0) (1.0.8)\r\n",
      "Requirement already satisfied: efficientnet==1.0.0 in /opt/conda/lib/python3.7/site-packages (from segmentation-models==1.0.0) (1.0.0)\r\n",
      "Requirement already satisfied: image-classifiers==1.0.0 in /opt/conda/lib/python3.7/site-packages (from segmentation-models==1.0.0) (1.0.0)\r\n",
      "Requirement already satisfied: scikit-image in /opt/conda/lib/python3.7/site-packages (from efficientnet==1.0.0->segmentation-models==1.0.0) (0.19.3)\r\n",
      "Requirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation-models==1.0.0) (3.7.0)\r\n",
      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->segmentation-models==1.0.0) (1.21.6)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (21.3)\r\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (9.1.1)\r\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (1.3.0)\r\n",
      "Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (2.5)\r\n",
      "Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (2.19.3)\r\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (2021.11.2)\r\n",
      "Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (1.7.3)\r\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.7/site-packages (from networkx>=2.2->scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (5.1.1)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->scikit-image->efficientnet==1.0.0->segmentation-models==1.0.0) (3.0.9)\r\n",
      "Installing collected packages: segmentation-models\r\n",
      "Successfully installed segmentation-models-1.0.0\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -U ../input/kerasapplications/Keras_Applications-1.0.8-py3-none-any.whl\n",
    "!pip install ../input/qubvel/efficientnet-1.0.0-py3-none-any.whl\n",
    "!pip install ../input/qubvel/image_classifiers-1.0.0-py3-none-any.whl\n",
    "!pip install ../input/qubvel/segmentation_models-1.0.0-py3-none-any.whl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f37c4d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-13T16:22:32.134513Z",
     "iopub.status.busy": "2022-09-13T16:22:32.134202Z",
     "iopub.status.idle": "2022-09-13T16:22:39.967919Z",
     "shell.execute_reply": "2022-09-13T16:22:39.966932Z"
    },
    "papermill": {
     "duration": 7.840922,
     "end_time": "2022-09-13T16:22:39.970509",
     "exception": false,
     "start_time": "2022-09-13T16:22:32.129587",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import tifffile as tiff\n",
    "from tqdm.auto import tqdm\n",
    "from glob import glob\n",
    "import gc\n",
    "import os\n",
    "import warnings \n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "gc.enable()\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "import segmentation_models as sm\n",
    "#from efficientnet.tfkeras import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7 \n",
    "\n",
    "import albumentations as A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6c0c85f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-13T16:22:39.978976Z",
     "iopub.status.busy": "2022-09-13T16:22:39.978435Z",
     "iopub.status.idle": "2022-09-13T16:22:39.983321Z",
     "shell.execute_reply": "2022-09-13T16:22:39.982329Z"
    },
    "papermill": {
     "duration": 0.01138,
     "end_time": "2022-09-13T16:22:39.985383",
     "exception": false,
     "start_time": "2022-09-13T16:22:39.974003",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cfg = {\n",
    "    'train_df': '../input/hubmap-organ-segmentation/train.csv',\n",
    "    'train_img': '../input/hubmap-organ-segmentation/train_images/',\n",
    "    'train_annot': '../input/hubmap-organ-segmentation/train_annotations/',\n",
    "    'img_shape': 384,\n",
    "    'thresh': 0.3\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "20d5a319",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-13T16:22:39.993209Z",
     "iopub.status.busy": "2022-09-13T16:22:39.992912Z",
     "iopub.status.idle": "2022-09-13T16:22:40.003904Z",
     "shell.execute_reply": "2022-09-13T16:22:40.002973Z"
    },
    "papermill": {
     "duration": 0.017289,
     "end_time": "2022-09-13T16:22:40.005961",
     "exception": false,
     "start_time": "2022-09-13T16:22:39.988672",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def dice_coef(y_true, y_pred, smooth=1):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "\n",
    "def iou_coef(y_true, y_pred, smooth=1):\n",
    "    intersection = K.sum(K.abs(y_true * y_pred), axis=[1,2,3])\n",
    "    union = K.sum(y_true,[1,2,3])+K.sum(y_pred,[1,2,3])-intersection\n",
    "    iou = K.mean((intersection + smooth) / (union + smooth), axis=0)\n",
    "    return iou\n",
    "\n",
    "\n",
    "def dice_loss(y_true, y_pred):\n",
    "    y_true = tf.cast(y_true, tf.float32)\n",
    "    smooth = 1.\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = y_true_f * y_pred_f\n",
    "    score = (2. * K.sum(intersection) + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "    return 1. - score\n",
    "\n",
    "def bce_dice_loss(y_true, y_pred):\n",
    "    return binary_crossentropy(tf.cast(y_true, tf.float32), y_pred) + 0.5 * dice_loss(tf.cast(y_true, tf.float32), y_pred)\n",
    "\n",
    "def weighted_loss(y_true, y_pred):\n",
    "    # Calculate the base loss\n",
    "    ce = K.sparse_categorical_crossentropy(y_true, y_pred)\n",
    "    # Apply the weights\n",
    "    one_weight = 1.0\n",
    "    zero_weight = 1e-2\n",
    "    weight_vector = y_true * one_weight + (1. - y_true) * zero_weight\n",
    "    weight_vector = K.squeeze(weight_vector, axis=-1)\n",
    "    weighted_ce = weight_vector * ce\n",
    "\n",
    "    # Return the mean error\n",
    "    return K.mean(weighted_ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "515143e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-13T16:22:40.013841Z",
     "iopub.status.busy": "2022-09-13T16:22:40.013581Z",
     "iopub.status.idle": "2022-09-13T16:22:40.021547Z",
     "shell.execute_reply": "2022-09-13T16:22:40.020706Z"
    },
    "papermill": {
     "duration": 0.014163,
     "end_time": "2022-09-13T16:22:40.023528",
     "exception": false,
     "start_time": "2022-09-13T16:22:40.009365",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def mask2rle(mask, orig_dim=160):\n",
    "    #Rescale image to original size\n",
    "    size = int(len(mask.flatten())**.5)\n",
    "    n = Image.fromarray(mask.reshape((size, size))*255.0)\n",
    "    n = n.resize((orig_dim, orig_dim))\n",
    "    n = np.array(n).astype(np.float32)\n",
    "    #Get pixels to flatten\n",
    "    pixels = n.T.flatten()\n",
    "    #Round the pixels using the half of the range of pixel value\n",
    "    pixels = (pixels-min(pixels) > ((max(pixels)-min(pixels))/2)).astype(int)\n",
    "    pixels = np.nan_to_num(pixels) #incase of zero-div-error\n",
    "    \n",
    "    pixels = np.concatenate([[0], pixels, [0]])\n",
    "    runs = np.where(pixels[1:] != pixels[:-1])[0]\n",
    "    runs[1::2] -= runs[::2]\n",
    "    \n",
    "    return ' '.join(str(x) for x in runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "edce163d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-13T16:22:40.031575Z",
     "iopub.status.busy": "2022-09-13T16:22:40.031327Z",
     "iopub.status.idle": "2022-09-13T16:23:15.463627Z",
     "shell.execute_reply": "2022-09-13T16:23:15.462413Z"
    },
    "papermill": {
     "duration": 35.439369,
     "end_time": "2022-09-13T16:23:15.466265",
     "exception": false,
     "start_time": "2022-09-13T16:22:40.026896",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d0e98ceb3ce4186a64e7e16464bce8a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>id</th>\n",
       "      <th>rle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10078</td>\n",
       "      <td>21803 4 21861 5 23824 66 25846 68 27869 69 298...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                                rle\n",
       "0  10078  21803 4 21861 5 23824 66 25846 68 27869 69 298..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model = tf.keras.models.load_model('../input/deeplabv3-bc-logits-false/best_model.h5', custom_objects={'dice_coef': dice_coef, 'iou_coef': iou_coef, 'dice_loss': dice_loss, 'bce_dice_loss': bce_dice_loss, 'weighted_loss': weighted_loss}, compile=False)\n",
    "model = tf.keras.models.load_model('../input/fpn-effnetb7-384p/best_model.h5', custom_objects={'binary_crossentropy_plus_jaccard_loss': sm.losses.bce_jaccard_loss, 'dice_coef': dice_coef, 'iou_coef': iou_coef, 'dice_loss': dice_loss, 'f1-score': sm.metrics.f1_score, 'binary_crossentropy_plus_dice_loss':sm.losses.bce_dice_loss})\n",
    "test_df = pd.read_csv('../input/hubmap-organ-segmentation/test.csv')\n",
    "sub = {'id':[], 'rle':[]}\n",
    "\n",
    "for _, row in tqdm(test_df.iterrows()):\n",
    "    path = '../input/hubmap-organ-segmentation/test_images/' + str(row['id']) + '.tiff'\n",
    "    img = Image.open(path)\n",
    "    img = img.resize((cfg['img_shape'], cfg['img_shape']))\n",
    "    img = np.array(img).reshape((1, cfg['img_shape'], cfg['img_shape'], 3))\n",
    "    img = img / 255.0\n",
    "    mask = model.predict(img)\n",
    "    #mask = np.where(mask >= cfg['thresh'], 1, 0)\n",
    "    \n",
    "    rle = mask2rle(np.round(mask).astype(np.float32), row['img_width']) \n",
    "    sub['id'].append(row['id'])\n",
    "    sub['rle'].append(rle)\n",
    "    \n",
    "    \n",
    "sub = pd.DataFrame(sub)\n",
    "sub.to_csv('submission.csv', index=False)\n",
    "sub"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 177.409086,
   "end_time": "2022-09-13T16:23:18.649395",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-09-13T16:20:21.240309",
   "version": "2.3.4"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "3c5c8f827603442fbb77af9e64a78a8f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "406fe13bac0c440d93c22d5a0c9cbef1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_41fbb80d7847404dafb3f5e331d9ade2",
       "max": 1.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_febb773321e740f28ba964dd43a4d035",
       "value": 1.0
      }
     },
     "41fbb80d7847404dafb3f5e331d9ade2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": "20px"
      }
     },
     "53c0e5f0112945f59e1b3144987aa6bf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_7804f7e7849242f49ded6f040f67bffc",
       "placeholder": "​",
       "style": "IPY_MODEL_68ef0bf67f17440fb2b56719001622bc",
       "value": " 1/? [00:12&lt;00:00, 12.87s/it]"
      }
     },
     "5d0e98ceb3ce4186a64e7e16464bce8a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_86c46c9311c5410d936dc8125b6e4887",
        "IPY_MODEL_406fe13bac0c440d93c22d5a0c9cbef1",
        "IPY_MODEL_53c0e5f0112945f59e1b3144987aa6bf"
       ],
       "layout": "IPY_MODEL_99254cddeb42435f8deda263e9b91003"
      }
     },
     "68ef0bf67f17440fb2b56719001622bc": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "7804f7e7849242f49ded6f040f67bffc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "86c46c9311c5410d936dc8125b6e4887": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_tooltip": null,
       "layout": "IPY_MODEL_3c5c8f827603442fbb77af9e64a78a8f",
       "placeholder": "​",
       "style": "IPY_MODEL_d19b58ddc21742e5bee3703d20c300de",
       "value": ""
      }
     },
     "99254cddeb42435f8deda263e9b91003": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "overflow_x": null,
       "overflow_y": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "d19b58ddc21742e5bee3703d20c300de": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "description_width": ""
      }
     },
     "febb773321e740f28ba964dd43a4d035": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}