nnunet_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import shutil\n",
    "from collections import OrderedDict\n",
    "import json\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "import SimpleITK as sitk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = '/home/mzukovec/Documents/Faks/Semester 3/AMS'\n",
    "respository_dir = os.path.join(base_dir,'nnUNet')\n",
    "os.chdir(base_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Folder Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_if_dont_exist(folder_path,overwrite=False):\n",
    "    \"\"\"\n",
    "    creates a folder if it does not exists\n",
    "    input: \n",
    "    folder_path : relative path of the folder which needs to be created\n",
    "    over_write :(default: False) if True overwrite the existing folder \n",
    "    \"\"\"\n",
    "    if os.path.exists(folder_path):\n",
    "        \n",
    "        if not overwrite:\n",
    "            print(f'{folder_path} exists.')\n",
    "        else:\n",
    "            print(f\"{folder_path} overwritten\")\n",
    "            shutil.rmtree(folder_path)\n",
    "            os.makedirs(folder_path)\n",
    "\n",
    "    else:\n",
    "      os.makedirs(folder_path)\n",
    "      print(f\"{folder_path} created!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1 exists.\n",
      "nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1/imagesTr exists.\n",
      "nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1/labelsTr exists.\n",
      "nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1/imagesTs exists.\n",
      "/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnunet_trained_models exists.\n"
     ]
    }
   ],
   "source": [
    "task_name = 'Task101_BRGR1' #Initial task\n",
    "nnunet_dir = \"nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data\"\n",
    "task_folder_name = os.path.join(nnunet_dir,task_name)\n",
    "train_image_dir = os.path.join(task_folder_name,'imagesTr')\n",
    "train_label_dir = os.path.join(task_folder_name,'labelsTr')\n",
    "test_dir = os.path.join(task_folder_name,'imagesTs')\n",
    "main_dir = os.path.join(base_dir,'nnUNet/nnunet')\n",
    "\n",
    "make_if_dont_exist(task_folder_name,overwrite = False)\n",
    "make_if_dont_exist(train_image_dir)\n",
    "make_if_dont_exist(train_label_dir)\n",
    "make_if_dont_exist(test_dir,overwrite= False)\n",
    "make_if_dont_exist(os.path.join(main_dir,'nnunet_trained_models'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['nnUNet_raw_data_base'] = os.path.join(main_dir,'nnUNet_raw_data_base')\n",
    "os.environ['nnUNet_preprocessed'] = os.path.join(main_dir,'preprocessed')\n",
    "os.environ['RESULTS_FOLDER'] = os.path.join(main_dir,'nnUNet_trained_models')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein \"Automated Design of Deep Learning Methods for Biomedical Image Segmentation\" arXiv preprint arXiv:1904.08128 (2020).\n",
      "If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n",
      "\n",
      "Verifying training set\n",
      "checking case BRGR1_001\n",
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      "Verifying label values\n",
      "Expected label values are [0, 1]\n",
      "Labels OK\n",
      "Verifying test set\n",
      "Dataset OK\n",
      "BRGR1_001\n",
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      "BRGR1_269\n",
      "BRGR1_270\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " Task101_BRGR1\n",
      "number of threads:  (8, 8) \n",
      "\n",
      "using nonzero mask for normalization\n",
      "Are we using the nonzero mask for normalizaion? OrderedDict([(0, True)])\n",
      "the median shape of the dataset is  [  1. 166. 143.]\n",
      "the max shape in the dataset is  [  1. 209. 174.]\n",
      "the min shape in the dataset is  [  1. 102.  81.]\n",
      "we don't want feature maps smaller than  4  in the bottleneck\n",
      "the transposed median shape of the dataset is  [  1. 166. 143.]\n",
      "generating configuration for 3d_fullres\n",
      "{0: {'batch_size': 14, 'num_pool_per_axis': [0, 1, 1], 'patch_size': array([  1, 166, 144]), 'median_patient_size_in_voxels': array([  1, 166, 143]), 'current_spacing': array([1., 1., 1.]), 'original_spacing': array([1., 1., 1.]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3]]}}\n",
      "transpose forward [0, 1, 2]\n",
      "transpose backward [0, 1, 2]\n",
      "Initializing to run preprocessing\n",
      "npz folder: /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_cropped_data/Task101_BRGR1\n",
      "output_folder: /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 129, 103)} \n",
      "after: no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 121, 96)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \n",
      "\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 135)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 121, 96)} \n",
      "\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 132, 101)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 135)} \n",
      "\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 166, 148)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 132, 101)} \n",
      "\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 139)} \n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 179, 146)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 166, 148)} \n",
      "\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 179, 146)} \n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 139)} \n",
      "\n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 165)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \n",
      "\n",
      "1 4002\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_046.npz\n",
      "1 4196\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_028.npz\n",
      "1 4067\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_055.npz\n",
      "1 3803\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_037.npz\n",
      "1 5878\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_064.npz\n",
      "1 6133\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_019.npz\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_001.npz\n",
      "1 7212\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_010.npz\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 132, 101)} \n",
      "after: no resampling necessary\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 132, 101)} \n",
      "\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 146, 113)} \n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 129, 103)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \n",
      "\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \n",
      "\n",
      "1 4236\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_056.npz\n",
      "1 4937\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_029.npz\n",
      "1 3947\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_047.npz\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 166, 148)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 166, 148)} \n",
      "\n",
      "no resampling necessary\n",
      "1 5300\n",
      "no resampling necessary\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_065.npz\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 135)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 135)} \n",
      "\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 139)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 139)} \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 179, 146)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 179, 146)} \n",
      "\n",
      "no resampling necessary\n",
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      "1 4136\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_038.npz\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 165)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \n",
      "\n",
      "1 6543\n",
      "no resampling necessary\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_002.npz\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 129, 103)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \n",
      "\n",
      "no resampling necessary\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_020.npz\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 195, 165)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 195, 165)} \n",
      "\n",
      "1 3859\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_048.npz\n",
      "1 6833\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_011.npz\n",
      "no resampling necessary\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_057.npz\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 146, 113)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \n",
      "\n",
      "1 4790\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_030.npz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 166, 148)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 166, 148)} \r\n",
      "\r\n",
      "1 5008\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_066.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 135)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 135)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 129, 103)} \r\n",
      "after: no resampling necessary\r\n",
      "no resampling necessary\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \r\n",
      "\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 139)} \r\n",
      "after: 1 4108\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_039.npz\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 139)} \r\n",
      "\r\n",
      "1 4255\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_049.npz\r\n",
      "1 6340\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_003.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 146, 113)} \r\n",
      "after: no resampling necessary\r\n",
      "no resampling necessary\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \r\n",
      "\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 179, 146)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 179, 146)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 165)} \r\n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 195, 165)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \r\n",
      "\r\n",
      "1 4782\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_031.npz\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 195, 165)} \r\n",
      "\r\n",
      "1 6427\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_021.npz\r\n",
      "1 6282\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_058.npz\r\n",
      "1 8195\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_012.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 132, 101)} \r\n",
      "after: no resampling necessary\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 132, 101)} \r\n",
      "\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 166, 148)} \r\n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 135)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 166, 148)} \r\n",
      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 135)} \r\n",
      "\r\n",
      "1 4025\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_050.npz\r\n",
      "1 4431\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_040.npz\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_067.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 139)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 139)} \r\n",
      "\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_004.npz\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 139)} \r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \r\n",
      "\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_013.npz\r\n",
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      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 121, 96)} \r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \r\n",
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      "\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_052.npz\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 139)} \r\n",
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      "\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 172, 149)} \r\n",
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      "1 3210\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_100.npz\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 179, 146)} \r\n",
      "\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 143, 112)} \r\n",
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      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 183, 150)} \r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 126, 102)} \r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 172, 149)} \r\n",
      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 102, 88)} \r\n",
      "\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_097.npz\r\n",
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      "\r\n",
      "1 4969\r\n",
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      "no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_137.npz\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      "1 3348\r\n",
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      "1 5519\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_087.npz\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_099.npz\r\n",
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      "no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_088.npz\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 103, 81)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_089.npz\r\n",
      "no resampling necessary\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_080.npz\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "no resampling necessary\r\n",
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      "\r\n"
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_210.npz\r\n",
      "no resampling necessary\r\n",
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      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 163, 139)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_162.npz\r\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "no resampling necessary\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      "after: no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_198.npz\r\n",
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      "no resampling necessary\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 182, 154)} \r\n",
      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 196, 171)} \r\n",
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      "no resampling necessary\r\n",
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      "after: no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
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      "after: no resampling necessary\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 150, 122)} \r\n",
      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_236.npz\r\n",
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      "no resampling necessary\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
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      "\r\n",
      "no resampling necessary\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 150, 122)} \r\n",
      "\r\n",
      "1 4481\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_265.npz\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 174)} \r\n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 167, 143)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 174)} \r\n",
      "\r\n",
      "1 4682\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_248.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
      "after: 1 10000\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_238.npz\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
      "\r\n",
      "1 5545\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_256.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 199, 154)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
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      "\r\n",
      "1 8551\r\n",
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      "1 3931\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_266.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 174)} \r\n",
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      "\r\n",
      "1 9757\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_233.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 167, 143)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
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      "1 5199\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_249.npz\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 138, 117)} \r\n",
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      "\r\n",
      "1 6691\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_257.npz\r\n",
      "1 4024\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 138, 117)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 138, 117)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
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      "1 3600\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_240.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 167, 143)} \r\n",
      "after: no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
      "\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
      "\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 174)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 199, 154)} \r\n",
      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 174)} \r\n",
      "\r\n",
      "1 7499\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_268.npz\r\n",
      "1 5724\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_250.npz\r\n",
      "1 6407\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_258.npz\r\n",
      "1 9317\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_234.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 138, 117)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 138, 117)} \r\n",
      "\r\n",
      "1 3674\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_241.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 138, 117)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 138, 117)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 167, 143)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
      "\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 199, 154)} \r\n",
      "\r\n",
      "1 3385\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_242.npz\r\n",
      "1 5631\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_251.npz\r\n",
      "1 6905\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_259.npz\r\n",
      "1 6902\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_269.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 138, 117)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 138, 117)} \r\n",
      "\r\n",
      "1 3544\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_243.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 167, 143)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
      "\r\n",
      "1 5566\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_252.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 150, 122)} \r\n",
      "\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 199, 154)} \r\n",
      "\r\n",
      "1 4314\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_260.npz\r\n",
      "1 6957\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_270.npz\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 150, 122)} \n",
      "\n",
      "1 4000\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_stage0/BRGR1_261.npz\n",
      "using nonzero mask for normalization\n",
      "Are we using the nonzero maks for normalizaion? OrderedDict([(0, True)])\n",
      "the median shape of the dataset is  [  1. 166. 143.]\n",
      "the max shape in the dataset is  [  1. 209. 174.]\n",
      "the min shape in the dataset is  [  1. 102.  81.]\n",
      "we don't want feature maps smaller than  4  in the bottleneck\n",
      "the transposed median shape of the dataset is  [  1. 166. 143.]\n",
      "[{'batch_size': 11, 'num_pool_per_axis': [5, 5], 'patch_size': array([192, 160]), 'median_patient_size_in_voxels': array([  1, 166, 143]), 'current_spacing': array([1., 1., 1.]), 'original_spacing': array([1., 1., 1.]), 'pool_op_kernel_sizes': [[2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'do_dummy_2D_data_aug': False}]\n",
      "Initializing to run preprocessing\n",
      "npz folder: /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_cropped_data/Task101_BRGR1\n",
      "output_folder: /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 121, 96)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 121, 96)} \n",
      "no resampling necessary\n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "normalization...\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "normalization done\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 135)} \n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 164, 139)} \n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 179, 146)} \n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 165)} \n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 132, 101)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 139)} \n",
      "\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 179, 146)} \n",
      "\n",
      "normalization...\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 135)} \n",
      "\n",
      "normalization...\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 132, 101)} \n",
      "\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \n",
      "\n",
      "normalization...\n",
      "normalization...\n",
      "normalization...\n",
      "no resampling necessary\n",
      "normalization done\n",
      "normalization done\n",
      "normalization done\n",
      "normalization done\n",
      "normalization done\n",
      "1 4196\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_028.npz\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 129, 103)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \n",
      "\n",
      "normalization...\n",
      "1 4067\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_055.npz\n",
      "1 3803\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_037.npz\n",
      "normalization done\n",
      "1 6720\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_001.npz\n",
      "1 6133\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_019.npz\n",
      "1 7212\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_010.npz\n",
      "1 4002\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_046.npz\n",
      "no resampling necessary\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 166, 148)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 132, 101)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 179, 146)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \n",
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      "normalization done\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 146, 113)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 164, 135)} \n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 165)} \n",
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      "\n",
      "normalization...\n",
      "normalization done\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 129, 103)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_027.npz\r\n",
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      "\r\n",
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      "\r\n",
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      "\r\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_146.npz\r\n",
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      "normalization done\r\n",
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      "no resampling necessary\r\n",
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      "normalization done\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 183, 150)} \r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 188, 155)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_122.npz\r\n",
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      "\r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 188, 155)} \r\n",
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      "\r\n",
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      "1 3768\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 186, 158)} \r\n",
      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 139, 125)} \r\n",
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      "1 7749\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_157.npz\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_165.npz\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 163, 139)} \r\n",
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      "\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_199.npz\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 139, 125)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_174.npz\r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 143, 113)} \r\n",
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     "output_type": "stream",
     "text": [
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_202.npz\r\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 202, 170)} \r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 196, 171)} \r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 174)} \r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
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      "\r\n",
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      "\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 170, 146)} \r\n",
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      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
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      "\r\n",
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      "\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_216.npz\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_232.npz\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
      "\r\n",
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      "no resampling necessary\r\n",
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      "after: normalization done\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_271.npz\r\n",
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      "\r\n",
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      "\r\n",
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      "no resampling necessary\r\n",
      "no resampling necessary\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 199, 154)} \r\n",
      "\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_267.npz\r\n",
      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 203, 174)} \r\n",
      "after: before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 203, 174)} \r\n",
      "\r\n",
      "normalization...\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
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      "1 6691\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_257.npz\r\n",
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      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_241.npz\r\n",
      "no resampling necessary\r\n",
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      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 167, 143)} \r\n",
      "after: normalization done\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 167, 143)} \r\n",
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      "\r\n",
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      "normalization done\r\n",
      "1 6902\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_269.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 168, 149)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 168, 149)} \r\n",
      "\r\n",
      "normalization...\r\n",
      "normalization done\r\n",
      "1 6905\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_259.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 199, 154)} \r\n",
      "after: no resampling necessary\r\n",
      "no resampling necessary\r\n",
      " {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 199, 154)} \r\n",
      "\r\n",
      "normalization...\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 150, 122)} \r\n",
      "\r\n",
      "normalization...\r\n",
      "normalization done\r\n",
      "normalization done\r\n",
      "1 4314\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_260.npz\r\n",
      "1 6957\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_270.npz\r\n",
      "no resampling necessary\r\n",
      "no resampling necessary\r\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 150, 122)} \r\n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 150, 122)} \r\n",
      "\r\n",
      "normalization...\r\n",
      "normalization done\r\n",
      "1 4000\r\n",
      "saving:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D_stage0/BRGR1_261.npz\r\n"
     ]
    }
   ],
   "source": [
    "#running it from the experiment_planning folder to verify the path settings\n",
    "os.chdir(main_dir)\n",
    "!python3 experiment_planning/nnUNet_plan_and_preprocess.py -t 101 --verify_dataset_integrity\n",
    "os.chdir(base_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein \"Automated Design of Deep Learning Methods for Biomedical Image Segmentation\" arXiv preprint arXiv:1904.08128 (2020).\n",
      "If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n",
      "\n",
      "###############################################\n",
      "I am running the following nnUNet: 2d\n",
      "My trainer class is:  <class 'nnunet.training.network_training.nnUNetTrainerV2.nnUNetTrainerV2'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  1\n",
      "modalities:  {0: 'MRI'}\n",
      "use_mask_for_norm OrderedDict([(0, True)])\n",
      "keep_only_largest_region None\n",
      "min_region_size_per_class None\n",
      "min_size_per_class None\n",
      "normalization_schemes OrderedDict([(0, 'nonCT')])\n",
      "stages...\n",
      "\n",
      "stage:  0\n",
      "{'batch_size': 11, 'num_pool_per_axis': [5, 5], 'patch_size': array([192, 160]), 'median_patient_size_in_voxels': array([  1, 166, 143]), 'current_spacing': array([1., 1., 1.]), 'original_spacing': array([1., 1., 1.]), 'pool_op_kernel_sizes': [[2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'do_dummy_2D_data_aug': False}\n",
      "\n",
      "I am using stage 0 from these plans\n",
      "I am using batch dice + CE loss\n",
      "\n",
      "I am using data from this folder:  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/preprocessed/Task101_BRGR1/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2020-11-24 12:11:15.774935: Creating new split...\n",
      "unpacking dataset\n",
      "done\n",
      "2020-11-24 12:11:20.486271: lr: 0.01\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "2020-11-24 12:11:21.656058: Unable to plot network architecture:\n",
      "2020-11-24 12:11:21.656232: No module named 'hiddenlayer'\n",
      "2020-11-24 12:11:21.656303: \n",
      "printing the network instead:\n",
      "\n",
      "2020-11-24 12:11:21.656366: Generic_UNet(\n",
      "  (conv_blocks_localization): ModuleList(\n",
      "    (0): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(960, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(512, 256, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(256, 128, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(128, 64, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(64, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (conv_blocks_context): ModuleList(\n",
      "    (0): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(1, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(32, 32, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (1): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(32, 64, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (2): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(64, 128, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (3): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(128, 256, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (4): StackedConvLayers(\n",
      "      (blocks): Sequential(\n",
      "        (0): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(256, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "        (1): ConvDropoutNormNonlin(\n",
      "          (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "          (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "          (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (5): Sequential(\n",
      "      (0): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=[2, 2], padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (1): StackedConvLayers(\n",
      "        (blocks): Sequential(\n",
      "          (0): ConvDropoutNormNonlin(\n",
      "            (conv): Conv2d(480, 480, kernel_size=[3, 3], stride=(1, 1), padding=[1, 1])\n",
      "            (instnorm): InstanceNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)\n",
      "            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (td): ModuleList()\n",
      "  (tu): ModuleList(\n",
      "    (0): ConvTranspose2d(480, 480, kernel_size=[2, 2], stride=[2, 2], bias=False)\n",
      "    (1): ConvTranspose2d(480, 256, kernel_size=[2, 2], stride=[2, 2], bias=False)\n",
      "    (2): ConvTranspose2d(256, 128, kernel_size=[2, 2], stride=[2, 2], bias=False)\n",
      "    (3): ConvTranspose2d(128, 64, kernel_size=[2, 2], stride=[2, 2], bias=False)\n",
      "    (4): ConvTranspose2d(64, 32, kernel_size=[2, 2], stride=[2, 2], bias=False)\n",
      "  )\n",
      "  (seg_outputs): ModuleList(\n",
      "    (0): Conv2d(480, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 2, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2020-11-24 12:11:21.659248: \n",
      "\n",
      "2020-11-24 12:11:21.659418: \n",
      "epoch:  0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-11-24 12:12:00.356901: train loss : -0.4273\n",
      "2020-11-24 12:12:02.984953: validation loss: -0.6616\n",
      "2020-11-24 12:12:02.985271: Average global foreground Dice: [0.8417973391404374]\n",
      "2020-11-24 12:12:02.985322: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:12:03.299071: lr: 0.009991\n",
      "2020-11-24 12:12:03.299206: This epoch took 41.639719 s\n",
      "\n",
      "2020-11-24 12:12:03.299251: \n",
      "epoch:  1\n",
      "2020-11-24 12:12:40.855994: train loss : -0.6883\n",
      "2020-11-24 12:12:43.557353: validation loss: -0.7154\n",
      "2020-11-24 12:12:43.557669: Average global foreground Dice: [0.8647717911203356]\n",
      "2020-11-24 12:12:43.557717: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:12:43.929442: lr: 0.009982\n",
      "2020-11-24 12:12:43.952988: saving checkpoint...\n",
      "2020-11-24 12:12:44.151056: done, saving took 0.22 seconds\n",
      "2020-11-24 12:12:44.155622: This epoch took 40.856333 s\n",
      "\n",
      "2020-11-24 12:12:44.155673: \n",
      "epoch:  2\n",
      "2020-11-24 12:13:21.929722: train loss : -0.7333\n",
      "2020-11-24 12:13:24.640467: validation loss: -0.7351\n",
      "2020-11-24 12:13:24.640786: Average global foreground Dice: [0.8751511491902731]\n",
      "2020-11-24 12:13:24.640834: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:13:25.030774: lr: 0.009973\n",
      "2020-11-24 12:13:25.053489: saving checkpoint...\n",
      "2020-11-24 12:13:25.511629: done, saving took 0.48 seconds\n",
      "2020-11-24 12:13:25.533152: This epoch took 41.377406 s\n",
      "\n",
      "2020-11-24 12:13:25.533444: \n",
      "epoch:  3\n",
      "2020-11-24 12:14:03.129440: train loss : -0.7496\n",
      "2020-11-24 12:14:05.747863: validation loss: -0.7441\n",
      "2020-11-24 12:14:05.748169: Average global foreground Dice: [0.8795742278624852]\n",
      "2020-11-24 12:14:05.748225: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:14:06.139527: lr: 0.009964\n",
      "2020-11-24 12:14:06.162621: saving checkpoint...\n",
      "2020-11-24 12:14:06.615065: done, saving took 0.48 seconds\n",
      "2020-11-24 12:14:06.644792: This epoch took 41.111123 s\n",
      "\n",
      "2020-11-24 12:14:06.645188: \n",
      "epoch:  4\n",
      "2020-11-24 12:14:44.050217: train loss : -0.7581\n",
      "2020-11-24 12:14:46.852941: validation loss: -0.7551\n",
      "2020-11-24 12:14:46.853250: Average global foreground Dice: [0.8850183611642948]\n",
      "2020-11-24 12:14:46.853313: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:14:47.243067: lr: 0.009955\n",
      "2020-11-24 12:14:47.266065: saving checkpoint...\n",
      "2020-11-24 12:14:47.697928: done, saving took 0.45 seconds\n",
      "2020-11-24 12:14:47.720112: This epoch took 41.074574 s\n",
      "\n",
      "2020-11-24 12:14:47.720428: \n",
      "epoch:  5\n",
      "2020-11-24 12:15:25.242776: train loss : -0.7676\n",
      "2020-11-24 12:15:27.874607: validation loss: -0.7506\n",
      "2020-11-24 12:15:27.874975: Average global foreground Dice: [0.8818703986775858]\n",
      "2020-11-24 12:15:27.875072: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:15:28.267730: lr: 0.009946\n",
      "2020-11-24 12:15:28.291606: saving checkpoint...\n",
      "2020-11-24 12:15:28.760765: done, saving took 0.49 seconds\n",
      "2020-11-24 12:15:28.767318: This epoch took 41.046673 s\n",
      "\n",
      "2020-11-24 12:15:28.767417: \n",
      "epoch:  6\n",
      "2020-11-24 12:16:05.002514: train loss : -0.7724\n",
      "2020-11-24 12:16:07.572343: validation loss: -0.7672\n",
      "2020-11-24 12:16:07.572664: Average global foreground Dice: [0.8896529410994634]\n",
      "2020-11-24 12:16:07.572712: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:16:07.965928: lr: 0.009937\n",
      "2020-11-24 12:16:07.992203: saving checkpoint...\n",
      "2020-11-24 12:16:08.428717: done, saving took 0.46 seconds\n",
      "2020-11-24 12:16:08.446484: This epoch took 39.678995 s\n",
      "\n",
      "2020-11-24 12:16:08.446561: \n",
      "epoch:  7\n",
      "2020-11-24 12:16:44.827317: train loss : -0.7754\n",
      "2020-11-24 12:16:47.402989: validation loss: -0.7641\n",
      "2020-11-24 12:16:47.403282: Average global foreground Dice: [0.8878446272334489]\n",
      "2020-11-24 12:16:47.403330: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:16:47.788778: lr: 0.009928\n",
      "2020-11-24 12:16:47.812005: saving checkpoint...\n",
      "2020-11-24 12:16:48.243220: done, saving took 0.45 seconds\n",
      "2020-11-24 12:16:48.266482: This epoch took 39.819865 s\n",
      "\n",
      "2020-11-24 12:16:48.266777: \n",
      "epoch:  8\n",
      "2020-11-24 12:17:24.454866: train loss : -0.7770\n",
      "2020-11-24 12:17:27.040167: validation loss: -0.7686\n",
      "2020-11-24 12:17:27.040474: Average global foreground Dice: [0.8905742394340792]\n",
      "2020-11-24 12:17:27.040535: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2020-11-24 12:17:27.430135: lr: 0.009919\n",
      "2020-11-24 12:17:27.453377: saving checkpoint...\n",
      "2020-11-24 12:17:27.914613: done, saving took 0.48 seconds\n",
      "2020-11-24 12:17:27.920502: This epoch took 39.653522 s\n",
      "\n",
      "2020-11-24 12:17:27.920603: \n",
      "epoch:  9\n",
      "^C\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/mzukovec/.local/bin/nnUNet_train\", line 11, in <module>\n",
      "    load_entry_point('nnunet', 'console_scripts', 'nnUNet_train')()\n",
      "  File \"/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/run/run_training.py\", line 151, in main\n",
      "    trainer.run_training()\n",
      "  File \"/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/training/network_training/nnUNetTrainerV2.py\", line 424, in run_training\n",
      "    ret = super().run_training()\n",
      "  File \"/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/training/network_training/nnUNetTrainer.py\", line 316, in run_training\n",
      "    super(nnUNetTrainer, self).run_training()\n",
      "  File \"/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/training/network_training/network_trainer.py\", line 440, in run_training\n",
      "    l = self.run_iteration(self.tr_gen, True)\n",
      "  File \"/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/training/network_training/nnUNetTrainerV2.py\", line 245, in run_iteration\n",
      "    torch.nn.utils.clip_grad_norm_(self.network.parameters(), 12)\n",
      "  File \"/home/mzukovec/.local/lib/python3.8/site-packages/torch/nn/utils/clip_grad.py\", line 38, in clip_grad_norm_\n",
      "    if clip_coef < 1:\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "os.chdir(main_dir)\n",
    "!nnUNet_train 2d nnUNetTrainerV2 101 0 \n",
    "os.chdir(base_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mv: cannot stat 'model_best.model': No such file or directory\n",
      "mv: cannot stat 'model_best.model.pkl': No such file or directory\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein \"Automated Design of Deep Learning Methods for Biomedical Image Segmentation\" arXiv preprint arXiv:1904.08128 (2020).\n",
      "If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n",
      "\n",
      "using model stored in  /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_trained_models/nnUNet/2d/Task101_BRGR1/nnUNetTrainerV2__nnUNetPlansv2.1\n",
      "This model expects 1 input modalities for each image\n",
      "Found 1 unique case ids, here are some examples: ['BRGR1_001']\n",
      "If they don't look right, make sure to double check your filenames. They must end with _0000.nii.gz etc\n",
      "number of cases: 1\n",
      "number of cases that still need to be predicted: 1\n",
      "emptying cuda cache\n",
      "loading parameters for folds, None\n",
      "folds is None so we will automatically look for output folders (not using 'all'!)\n",
      "found the following folds:  ['/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_trained_models/nnUNet/2d/Task101_BRGR1/nnUNetTrainerV2__nnUNetPlansv2.1/fold_0']\n",
      "using the following model files:  ['/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_trained_models/nnUNet/2d/Task101_BRGR1/nnUNetTrainerV2__nnUNetPlansv2.1/fold_0/model_final_checkpoint.model']\n",
      "starting preprocessing generator\n",
      "starting prediction...\n",
      "preprocessing nnUNet_Prediction_Results/Task101_BRGR1/BRGR1_001.nii.gz\n",
      "using preprocessor PreprocessorFor2D\n",
      "before crop: (1, 1, 256, 256) after crop: (1, 1, 182, 159) spacing: [1. 1. 1.] \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([1., 1., 1.]), 'spacing_transposed': array([1., 1., 1.]), 'data.shape (data is transposed)': (1, 1, 182, 159)} \n",
      "after:  {'spacing': array([1., 1., 1.]), 'data.shape (data is resampled)': (1, 1, 182, 159)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "(1, 1, 182, 159)\n",
      "This worker has ended successfully, no errors to report\n",
      "predicting nnUNet_Prediction_Results/Task101_BRGR1/BRGR1_001.nii.gz\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "inference done. Now waiting for the segmentation export to finish...\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "WARNING! Cannot run postprocessing because the postprocessing file is missing. Make sure to run consolidate_folds in the output folder of the model first!\n",
      "The folder you need to run this in is /home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_trained_models/nnUNet/2d/Task101_BRGR1/nnUNetTrainerV2__nnUNetPlansv2.1\n"
     ]
    }
   ],
   "source": [
    "#location where you want save your results, will be created if dont exist\n",
    "os.chdir(os.path.join(main_dir,'nnUNet_trained_models', 'nnUNet', '2d', task_name, 'nnUNetTrainerV2__nnUNetPlansv2.1', 'fold_0'))\n",
    "!mv model_best.model model_final_checkpoint.model\n",
    "!mv model_best.model.pkl model_final_checkpoint.model.pkl\n",
    "os.chdir(main_dir)\n",
    "!nnUNet_predict -i nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1/imagesTs -o nnUNet_Prediction_Results/Task101_BRGR1 -t 101 -tr nnUNetTrainerV2 -m 2d --num_threads_preprocessing 1 --save_npz\n",
    "os.chdir(base_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1/imagesTs')\n",
    "tmp = sitk.ReadImage('BRGR1_001_0000.nii.gz')\n",
    "tmp = sitk.GetArrayFromImage(tmp)\n",
    "if len(tmp.shape) < 3:\n",
    "    tmp = tmp.reshape((1, tmp.shape[0], tmp.shape[1]))\n",
    "    tmp = sitk.GetImageFromArray(tmp)\n",
    "    sitk.WriteImage(tmp, 'BRGR1_001_0000.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
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       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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width=\"952.65\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src = sitk.ReadImage('/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_raw_data_base/nnUNet_raw_data/Task101_BRGR1/imagesTs/BRGR1_001_0000.nii.gz')\n",
    "seg = sitk.ReadImage('/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_Prediction_Results/Task101_BRGR1/BRGR1_001.nii.gz')\n",
    "data = np.load('/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_Prediction_Results/Task101_BRGR1/BRGR1_001.npz')\n",
    "data = data['softmax'][0]\n",
    "plt.figure()\n",
    "plt.subplot(1,3,1)\n",
    "plt.title('Izvorna slika')\n",
    "plt.imshow(sitk.GetArrayFromImage(src).squeeze(), cmap='gray')\n",
    "plt.subplot(1,3,2)\n",
    "plt.title('Thresholded softmax')\n",
    "plt.imshow(sitk.GetArrayFromImage(seg).squeeze(), cmap='gray')\n",
    "plt.subplot(1,3,3)\n",
    "plt.title('Pure softmax')\n",
    "plt.imshow(np.array(data, dtype=np.float32)[:,:], cmap='jet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load('/home/mzukovec/Documents/Faks/Semester 3/AMS/nnUNet/nnunet/nnUNet_Prediction_Results/Task101_BRGR1/BRGR1_001.npz')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['softmax'][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}