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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "TorchIO_data_loading.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dT9Q8Wgy5UE1",
"outputId": "80ef29f0-c43a-4e97-8081-9b2537901316"
},
"source": [
"#Running this code and the cell below allows us to access files in drive\r\n",
"from google.colab import drive\r\n",
"drive.mount('/content/drive', force_remount=True)"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K13L9oVaA7qA",
"outputId": "eaab4f60-e67e-44e2-8813-4c738617b493"
},
"source": [
"cd drive/MyDrive/MacAI"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/drive/MyDrive/MacAI\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-cC2ulF95iiw",
"outputId": "087f877b-60db-46c9-81ca-9debebd85bae"
},
"source": [
"pip install torchio"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting torchio\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/3d/33/94812ae74a2815fdd5bf7c4e26be75086ebc770309c569380e6f7cc4ad60/torchio-0.18.29-py2.py3-none-any.whl (140kB)\n",
"\r\u001b[K |██▎ | 10kB 20.8MB/s eta 0:00:01\r\u001b[K |████▋ | 20kB 25.9MB/s eta 0:00:01\r\u001b[K |███████ | 30kB 31.8MB/s eta 0:00:01\r\u001b[K |█████████▎ | 40kB 21.9MB/s eta 0:00:01\r\u001b[K |███████████▋ | 51kB 12.5MB/s eta 0:00:01\r\u001b[K |██████████████ | 61kB 13.7MB/s eta 0:00:01\r\u001b[K |████████████████▎ | 71kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████▋ | 81kB 11.5MB/s eta 0:00:01\r\u001b[K |█████████████████████ | 92kB 12.6MB/s eta 0:00:01\r\u001b[K |███████████████████████▎ | 102kB 12.4MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 112kB 12.4MB/s eta 0:00:01\r\u001b[K |████████████████████████████ | 122kB 12.4MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▎ | 133kB 12.4MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 143kB 12.4MB/s \n",
"\u001b[?25hRequirement already satisfied: nibabel in /usr/local/lib/python3.7/dist-packages (from torchio) (3.0.2)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from torchio) (1.4.1)\n",
"Requirement already satisfied: humanize in /usr/local/lib/python3.7/dist-packages (from torchio) (0.5.1)\n",
"Requirement already satisfied: torch>=1.1 in /usr/local/lib/python3.7/dist-packages (from torchio) (1.7.1+cu101)\n",
"Collecting Deprecated\n",
" Downloading https://files.pythonhosted.org/packages/d4/56/7d4774533d2c119e1873993d34d313c9c9efc88c5e4ab7e33bdf915ad98c/Deprecated-1.2.11-py2.py3-none-any.whl\n",
"Requirement already satisfied: Click in /usr/local/lib/python3.7/dist-packages (from torchio) (7.1.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from torchio) (4.41.1)\n",
"Collecting SimpleITK<2\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/4a/ee/638b6bae2db10e5ef4ca94c95bb29ec25aa37a9d721b47f91077d7e985e0/SimpleITK-1.2.4-cp37-cp37m-manylinux1_x86_64.whl (42.5MB)\n",
"\u001b[K |████████████████████████████████| 42.5MB 102kB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchio) (1.19.5)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.1->torchio) (3.7.4.3)\n",
"Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.7/dist-packages (from Deprecated->torchio) (1.12.1)\n",
"Installing collected packages: Deprecated, SimpleITK, torchio\n",
"Successfully installed Deprecated-1.2.11 SimpleITK-1.2.4 torchio-0.18.29\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KDiN7ieXkvIt"
},
"source": [
"import matplotlib.pyplot as plt\r\n",
"import numpy as np\r\n",
"import torch\r\n",
"from pathlib import Path\r\n",
"from torchio.transforms import HistogramStandardization"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tUly42ud6QY_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a719b388-8750-48f4-8314-cde5f18e05ae"
},
"source": [
"import os\r\n",
"import torchio as tio\r\n",
"subjects = []\r\n",
"base_dir = './macai_datasets/brats_new/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/'\r\n",
"\r\n",
"flairs = []\r\n",
"t1s = []\r\n",
"t2s = []\r\n",
"t1ces = []\r\n",
"segs = []\r\n",
"\r\n",
"series_strings = ['flair', 't1', 't2', 't1ce', 'seg']\r\n",
"orig_paths = [flairs, t1s, t2s, t1ces, segs]\r\n",
"\r\n",
"for file in [file for file in os.listdir('./macai_datasets/brats_new/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData') if os.path.isdir(base_dir + file) == True]:\r\n",
" print(file)\r\n",
"\r\n",
" subject_dict = {}\r\n",
" for i, _ in enumerate(series_strings):\r\n",
" current_path = base_dir+file +'/'+ file+'_' + series_strings[i] +'.nii.gz'\r\n",
" orig_paths[i].append(current_path)\r\n",
" if series_strings[i] != 'seg':\r\n",
" subject_dict[series_strings[i]] = tio.ScalarImage(path=current_path)\r\n",
" else:\r\n",
" subject_dict[series_strings[i]] = tio.LabelMap(path=current_path)\r\n",
" subject = tio.Subject(subject_dict)\r\n",
"\r\n",
" subjects.append(subject)\r\n",
"dataset = tio.SubjectsDataset(subjects)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458
},
"id": "Lg3JKzRjaYL2",
"outputId": "23b2c4d2-6447-4e18-bbe2-1fac4c9ab8bc"
},
"source": [
"landmarks_dict = {}\r\n",
"landmarks_paths = [Path(modality + '_landmarks.npy') for modality in series_strings]\r\n",
"\r\n",
"series_strings = ['data', 'seg']\r\n",
"#some serialization problems right now but whatever\r\n",
"for i, series in enumerate(series_strings):\r\n",
" if series != 'seg':\r\n",
" print('Training/Loading Nyul for series: '+ series +'\\n')\r\n",
" landmarks_path = landmarks_paths[i]\r\n",
" landmarks = HistogramStandardization.train(orig_paths[i])\r\n",
" # (landmarks_path if landmarks_path.is_file()\r\n",
" # else \r\n",
" # )\r\n",
" torch.save(landmarks, landmarks_path)\r\n",
"\r\n",
" landmarks_dict[series] = landmarks\r\n"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"\r 0%| | 0/368 [00:00<?, ?it/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Training/Loading Nyul for series: flair\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
" 6%|▌ | 22/368 [00:30<07:25, 1.29s/it]"
],
"name": "stderr"
},
{
"output_type": "error",
"ename": "SystemError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/SimpleITK/SimpleITK.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 3708\u001b[0m \u001b[0m__swig_setmethods__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3709\u001b[0;31m \u001b[0m__setattr__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_swig_setattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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 3710\u001b[0m \u001b[0m__swig_getmethods__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-2a9f7d2b4d07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training/Loading Nyul for series: '\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mseries\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlandmarks_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlandmarks_paths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mlandmarks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHistogramStandardization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_paths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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 10\u001b[0m \u001b[0;31m# (landmarks_path if landmarks_path.is_file()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# else\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchio/transforms/preprocessing/intensity/histogram_standardization.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(cls, images_paths, cutoff, mask_path, masking_function, output_path)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0mpercentiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_percentiles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpercentiles_cutoff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_file_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m \u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_file_path\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 168\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmasking_function\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmasking_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchio/data/io.py\u001b[0m in \u001b[0;36mread_image\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTypePath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\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 18\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_sitk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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 20\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# try with NiBabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torchio/data/io.py\u001b[0m in \u001b[0;36m_read_sitk\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_dicom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msitk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReadImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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 51\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maffine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msitk_to_nib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_uint_to_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/SimpleITK/SimpleITK.py\u001b[0m in \u001b[0;36mReadImage\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 8874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8875\u001b[0m \"\"\"\n\u001b[0;32m-> 8876\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_SimpleITK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReadImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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 8877\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mImageViewer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_object\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 8878\u001b[0m \"\"\"\n",
"\u001b[0;31mSystemError\u001b[0m: <built-in function ReadImage> returned a result with an error set"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BAsNt9MRDo0k"
},
"source": [
"print(landmarks_dict)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vttA1NMTg3QW",
"outputId": "fc297b58-24a6-4556-9d34-4624ed3591aa"
},
"source": [
"print(dataset[0])"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"Subject(Keys: ('flair', 't1', 't2', 't1ce', 'seg'); images: 5)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KAIVJB31xtu6"
},
"source": [
"# RandomElasticDeformation takes forever, again. Maybe even longer than before.\r\n",
"\r\n",
"\r\n",
"transforms_dict = {\r\n",
" tio.HistogramStandardization(landmarks_dict):1,\r\n",
" tio.transforms.ZNormalization(): 1,\r\n",
" tio.transforms.RandomGamma(): 0.5,\r\n",
" # tio.transforms.RandomElasticDeformation(): 0.5\r\n",
"}\r\n",
"all_transforms = tio.transforms.Compose(transforms_dict)\r\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rZfgSs9iAOCk"
},
"source": [
"iteration =0\r\n",
"for i, _ in enumerate(dataset):\r\n",
" print(i)\r\n",
"\r\n",
" print(torch.sum(dataset[i]['seg'].tensor))\r\n",
" transformed = all_transforms(dataset[i]) \r\n",
" print(torch.sum(transformed['seg'].tensor))\r\n",
"\r\n",
" iteration += 1\r\n",
" if iteration >= 10:\r\n",
" break"
],
"execution_count": null,
"outputs": []
}
]
}