dl_part/unnet_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries and setting up enviroment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "import shutil\n",
    "from collections import OrderedDict\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "#visualization of the dataset\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import SimpleITK as sitk\n",
    "\n",
    "import sys\n",
    "import os\n",
    "sys.path.append('..')\n",
    "from utils import haussdorf, avd, rel_abs_vol_dif, dice_score, create_special_trainer, check_gpu, graph_brain, convert_data_structure, create_dataset_json\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running custom scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The file nnUNetTrainerV2_Fast exists, do nothing.\n"
     ]
    }
   ],
   "source": [
    "file_path = \"nnUNet/nnunet/training/network_training/nnUNetTrainerV2_Fast.py\"\n",
    "create_special_trainer(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New data structure already exists\n"
     ]
    }
   ],
   "source": [
    "convert_data_structure('../TrainingValidationTestSets/','nnUNet_raw/nnUNet_raw_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File 'nnUNet_raw/nnUNet_raw_data/Task975_BrainSegmentation/dataset.json' already exists. No action taken.\n"
     ]
    }
   ],
   "source": [
    "create_dataset_json('nnUNet_raw/nnUNet_raw_data/Task975_BrainSegmentation/', \"nnUNet_raw/nnUNet_raw_data/Task975_BrainSegmentation/dataset.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'nnUNet'...\n",
      "remote: Enumerating objects: 11351, done.\u001b[K\n",
      "remote: Counting objects: 100% (400/400), done.\u001b[K\n",
      "remote: Compressing objects: 100% (222/222), done.\u001b[K\n",
      "remote: Total 11351 (delta 244), reused 291 (delta 176), pack-reused 10951\u001b[K\n",
      "Receiving objects: 100% (11351/11351), 5.50 MiB | 6.94 MiB/s, done.\n",
      "Resolving deltas: 100% (8635/8635), done.\n"
     ]
    }
   ],
   "source": [
    "! git clone https://github.com/MIC-DKFZ/nnUNet.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already on 'nnunetv1'\n",
      "Your branch is up to date with 'origin/nnunetv1'.\n"
     ]
    }
   ],
   "source": [
    "! cd nnUNet; git checkout nnunetv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: SimpleITK in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (2.3.1)\n",
      "Requirement already satisfied: batchgenerators>=0.23 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (0.25)\n",
      "Requirement already satisfied: dicom2nifti in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (2.4.9)\n",
      "Requirement already satisfied: matplotlib in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (3.8.2)\n",
      "Requirement already satisfied: medpy in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (0.4.0)\n",
      "Requirement already satisfied: nibabel in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (5.1.0)\n",
      "Requirement already satisfied: numpy in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (1.26.2)\n",
      "Requirement already satisfied: pandas in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (2.1.3)\n",
      "Requirement already satisfied: requests in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (2.31.0)\n",
      "Requirement already satisfied: scikit-image>=0.14 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (0.22.0)\n",
      "Requirement already satisfied: scikit-learn in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (1.3.2)\n",
      "Requirement already satisfied: scipy in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (1.11.4)\n",
      "Requirement already satisfied: tifffile in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (2023.9.26)\n",
      "Requirement already satisfied: torch>1.10.0 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (2.1.1)\n",
      "Requirement already satisfied: tqdm in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nnunet==1.7.0) (4.66.1)\n",
      "Requirement already satisfied: future in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from batchgenerators>=0.23->nnunet==1.7.0) (0.18.3)\n",
      "Requirement already satisfied: pillow>=7.1.2 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from batchgenerators>=0.23->nnunet==1.7.0) (10.1.0)\n",
      "Requirement already satisfied: threadpoolctl in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from batchgenerators>=0.23->nnunet==1.7.0) (3.2.0)\n",
      "Requirement already satisfied: unittest2 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from batchgenerators>=0.23->nnunet==1.7.0) (1.1.0)\n",
      "Requirement already satisfied: networkx>=2.8 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from scikit-image>=0.14->nnunet==1.7.0) (3.2.1)\n",
      "Requirement already satisfied: packaging>=21 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from scikit-image>=0.14->nnunet==1.7.0) (23.2)\n",
      "Requirement already satisfied: imageio>=2.27 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from scikit-image>=0.14->nnunet==1.7.0) (2.33.0)\n",
      "Requirement already satisfied: lazy_loader>=0.3 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from scikit-image>=0.14->nnunet==1.7.0) (0.3)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (2.18.1)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (8.9.2.26)\n",
      "Requirement already satisfied: sympy in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (1.12)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (12.1.0.106)\n",
      "Requirement already satisfied: triton==2.1.0 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (2.1.0)\n",
      "Requirement already satisfied: typing-extensions in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (4.8.0)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (12.1.105)\n",
      "Requirement already satisfied: jinja2 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (3.1.2)\n",
      "Requirement already satisfied: fsspec in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (2023.10.0)\n",
      "Requirement already satisfied: filelock in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from torch>1.10.0->nnunet==1.7.0) (3.13.1)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>1.10.0->nnunet==1.7.0) (12.3.101)\n",
      "Requirement already satisfied: pydicom>=2.2.0 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from dicom2nifti->nnunet==1.7.0) (2.4.3)\n",
      "Requirement already satisfied: python-gdcm in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from dicom2nifti->nnunet==1.7.0) (3.0.22)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from matplotlib->nnunet==1.7.0) (0.12.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from matplotlib->nnunet==1.7.0) (1.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from matplotlib->nnunet==1.7.0) (1.4.5)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from matplotlib->nnunet==1.7.0) (4.45.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from matplotlib->nnunet==1.7.0) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from matplotlib->nnunet==1.7.0) (3.1.1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from pandas->nnunet==1.7.0) (2023.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from pandas->nnunet==1.7.0) (2023.3.post1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from requests->nnunet==1.7.0) (3.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from requests->nnunet==1.7.0) (2023.11.17)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from requests->nnunet==1.7.0) (2.1.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from requests->nnunet==1.7.0) (3.3.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from scikit-learn->nnunet==1.7.0) (1.3.2)\n",
      "Requirement already satisfied: six>=1.5 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->nnunet==1.7.0) (1.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from jinja2->torch>1.10.0->nnunet==1.7.0) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from sympy->torch>1.10.0->nnunet==1.7.0) (1.3.0)\n",
      "Collecting argparse\n",
      "  Using cached argparse-1.4.0-py2.py3-none-any.whl (23 kB)\n",
      "Requirement already satisfied: traceback2 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from unittest2->batchgenerators>=0.23->nnunet==1.7.0) (1.4.0)\n",
      "Requirement already satisfied: linecache2 in /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages (from traceback2->unittest2->batchgenerators>=0.23->nnunet==1.7.0) (1.0.0)\n",
      "Installing collected packages: argparse, nnunet\n",
      "  Attempting uninstall: nnunet\n",
      "    Found existing installation: nnunet 1.7.0\n",
      "    Uninstalling nnunet-1.7.0:\n",
      "      Successfully uninstalled nnunet-1.7.0\n",
      "  Running setup.py develop for nnunet\n",
      "Successfully installed argparse-1.4.0 nnunet-1.7.0\n"
     ]
    }
   ],
   "source": [
    "! cd nnUNet; pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jan  9 22:20:02 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  NVIDIA GeForce GTX 1050 Ti     Off | 00000000:01:00.0 Off |                  N/A |\n",
      "| N/A   43C    P8              N/A / ERR! |      6MiB /  4096MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|    0   N/A  N/A      2374      G   /usr/lib/xorg/Xorg                            4MiB |\n",
      "|    0   N/A  N/A      2626      G   ...libexec/gnome-remote-desktop-daemon        1MiB |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_____Python, Pytorch, Cuda info____\n",
      "__Python VERSION: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]\n",
      "__pyTorch VERSION: 2.1.1+cu121\n",
      "__CUDA RUNTIME API VERSION\n",
      "__CUDNN VERSION: 8902\n",
      "_____nvidia-smi GPU details____\n",
      "index, name, driver_version, memory.total [MiB], memory.used [MiB], memory.free [MiB]\n",
      "0, NVIDIA GeForce GTX 1050 Ti, 535.146.02, 4096 MiB, 6 MiB, 4034 MiB\n",
      "_____Device assignments____\n",
      "Number CUDA Devices: 1\n",
      "Current cuda device:  0  **May not correspond to nvidia-smi ID above, check visibility parameter\n",
      "Device name:  NVIDIA GeForce GTX 1050 Ti\n"
     ]
    }
   ],
   "source": [
    "check_gpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## nnUNet_raw should exists since it is on the github\n",
    "### Create Manually the other two folders the results and preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = os.getcwd()\n",
    "nnUNet_raw = os.path.join(base_dir,'nnUNet_raw')\n",
    "nnUNet_preprocessed = os.path.join(base_dir,'nnUNet_preprocessed')\n",
    "results_folder = os.path.join(base_dir,'nnUNet_results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_raw\n"
     ]
    }
   ],
   "source": [
    "print(nnUNet_raw) #Check the directory address is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"nnUNet_raw_data_base\"] = str(nnUNet_raw)\n",
    "os.environ[\"nnUNet_preprocessed\"] = str(nnUNet_preprocessed)\n",
    "os.environ[\"RESULTS_FOLDER\"] = str(results_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the data and preparing for the trainning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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 IBSR_01\n",
      "checking case IBSR_02\n",
      "checking case IBSR_03\n",
      "checking case IBSR_04\n",
      "checking case IBSR_05\n",
      "checking case IBSR_06\n",
      "checking case IBSR_07\n",
      "checking case IBSR_08\n",
      "checking case IBSR_09\n",
      "checking case IBSR_10\n",
      "checking case IBSR_11\n",
      "checking case IBSR_12\n",
      "checking case IBSR_13\n",
      "checking case IBSR_14\n",
      "checking case IBSR_15\n",
      "Verifying label values\n",
      "Expected label values are [0, 1, 2, 3]\n",
      "Labels OK\n",
      "Verifying test set\n",
      "Dataset OK\n",
      "IBSR_01\n",
      "IBSR_02\n",
      "IBSR_03\n",
      "IBSR_04\n",
      "IBSR_05\n",
      "IBSR_06\n",
      "IBSR_09\n",
      "IBSR_10\n",
      "IBSR_12IBSR_11\n",
      "\n",
      "IBSR_08\n",
      "IBSR_13IBSR_07\n",
      "\n",
      "IBSR_14\n",
      "IBSR_15\n",
      "\n",
      "\n",
      "\n",
      " Task975_BrainSegmentation\n",
      "number of threads:  (8, 8) \n",
      "\n",
      "using nonzero mask for normalization\n",
      "Are we using the nonzero mask for normalization? OrderedDict([(0, True)])\n",
      "the median shape of the dataset is  [138.66666667 115.         147.2       ]\n",
      "the max shape in the dataset is  [154.46429043 121.         153.6       ]\n",
      "the min shape in the dataset is  [131. 109. 136.]\n",
      "we don't want feature maps smaller than  4  in the bottleneck\n",
      "the transposed median shape of the dataset is  [115.         138.66666667 147.2       ]\n",
      "generating configuration for 3d_fullres\n",
      "{0: {'batch_size': 2, 'num_pool_per_axis': [4, 5, 5], 'patch_size': array([112, 128, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), 'do_dummy_2D_data_aug': False, 'pool_op_kernel_sizes': [[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}}\n",
      "transpose forward [1, 0, 2]\n",
      "transpose backward [1, 0, 2]\n",
      "Initializing to run preprocessing\n",
      "npz folder: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_raw/nnUNet_cropped_data/Task975_BrainSegmentation\n",
      "output_folder: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 111, 131, 143)}no resampling necessary \n",
      "after: \n",
      " no resampling necessary\n",
      "before: {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 111, 131, 143)}{'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 116, 134, 151)} \n",
      "after:   \n",
      "\n",
      "{'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 116, 134, 151)} \n",
      "\n",
      "{'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 111, 131, 143)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 111, 131, 143)} \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: no separate z, order 3\n",
      "no separate z, order 3\n",
      "{'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 119, 142, 136)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 119, 142, 136)} \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 115, 137, 140)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 137, 140)} \n",
      "\n",
      "no separate z, order 3\n",
      "1 6662\n",
      "1 6753\n",
      "1 8023\n",
      "1 10000\n",
      "1 10000\n",
      "2 10000\n",
      "2 10000\n",
      "2 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_02.npz\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_01.npz\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_04.npz\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_03.npz\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_05.npz\n",
      "no separate z, order 3\n",
      "no separate z, order 3\n",
      "no separate z, order 3\n",
      "no separate z, order 3\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 115, 136, 149)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 136, 149)} \n",
      "\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_13.npz\n",
      "no separate z, order 1\n",
      "no separate z, order 1\n",
      "no separate z, order 1\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 116, 144, 146)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 116, 144, 146)} \n",
      "\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_14.npz\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 115, 138, 138)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 147, 147)} \n",
      "\n",
      "1 10000\n",
      "no separate z, order 1\n",
      "no separate z, order 1\n",
      "no separate z, order 3\n",
      "2 10000\n",
      "no separate z, order 1\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_08.npz\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 113, 128, 132)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 113, 137, 141)} \n",
      "\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_06.npz\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 109, 130, 135)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 109, 139, 144)} \n",
      "\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_07.npz\n",
      "before: {'spacing': array([0.8370536, 1.5      , 0.8370536]), 'spacing_transposed': array([1.5      , 0.8370536, 0.8370536]), 'data.shape (data is transposed)': (1, 115, 159, 167)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 142, 149)} \n",
      "\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 109, 130, 144)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 109, 139, 154)} \n",
      "\n",
      "1 9419\n",
      "1 10000\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 111, 133, 143)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 111, 142, 153)} \n",
      "\n",
      "2 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_11.npz\n",
      "1 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_09.npz\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_12.npz\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([0.8370536, 1.5      , 0.8370536]), 'spacing_transposed': array([1.5      , 0.8370536, 0.8370536]), 'data.shape (data is transposed)': (1, 121, 173, 166)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 121, 154, 148)} \n",
      "\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_10.npz\n",
      "before: {'spacing': array([0.8370536, 1.5      , 0.8370536]), 'spacing_transposed': array([1.5      , 0.8370536, 0.8370536]), 'data.shape (data is transposed)': (1, 120, 165, 171)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 120, 147, 153)} \n",
      "\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_stage0/IBSR_15.npz\n",
      "using nonzero mask for normalization\n",
      "Are we using the nonzero mask for normalization? OrderedDict([(0, True)])\n",
      "the median shape of the dataset is  [138.66666667 115.         147.2       ]\n",
      "the max shape in the dataset is  [154.46429043 121.         153.6       ]\n",
      "the min shape in the dataset is  [131. 109. 136.]\n",
      "we don't want feature maps smaller than  4  in the bottleneck\n",
      "the transposed median shape of the dataset is  [115.         138.66666667 147.2       ]\n",
      "[{'batch_size': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_raw/nnUNet_cropped_data/Task975_BrainSegmentation\n",
      "output_folder: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 116, 134, 151)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 116, 134, 151)} \n",
      "\n",
      "no separate z, order 3\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 111, 131, 143)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 111, 131, 143)} \n",
      "\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: normalization...\n",
      "{'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 115, 137, 140)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 137, 140)} \n",
      "\n",
      "normalization...\n",
      "normalization...\n",
      "no resampling necessaryno resampling necessary\n",
      "\n",
      "no resampling necessaryno resampling necessary\n",
      "\n",
      "before: before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 119, 142, 136)}{'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 111, 131, 143)} \n",
      "after:   \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 119, 142, 136)}{'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 111, 131, 143)} \n",
      "\n",
      " \n",
      "\n",
      "no separate z, order 3\n",
      "normalization...\n",
      "normalization...\n",
      "no separate z, order 3\n",
      "normalization done\n",
      "normalization done\n",
      "normalization done\n",
      "1 6662\n",
      "normalization done\n",
      "1 8023\n",
      "1 10000\n",
      "normalization done\n",
      "1 6753\n",
      "1 10000\n",
      "2 10000\n",
      "2 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_02.npz\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_04.npz\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_03.npz\n",
      "2 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_01.npz\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_05.npz\n",
      "no separate z, order 3\n",
      "no separate z, order 3\n",
      "no separate z, order 3\n",
      "no separate z, order 3\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 115, 136, 149)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 136, 149)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_13.npz\n",
      "no separate z, order 1\n",
      "no separate z, order 1\n",
      "no resampling necessary\n",
      "no resampling necessary\n",
      "before: {'spacing': array([0.9375, 1.5   , 0.9375]), 'spacing_transposed': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is transposed)': (1, 116, 144, 146)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 116, 144, 146)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "no separate z, order 1\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_14.npz\n",
      "no separate z, order 1\n",
      "no separate z, order 1\n",
      "no separate z, order 1\n",
      "no separate z, order 3\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 115, 138, 138)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 147, 147)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 109, 130, 135)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 109, 139, 144)} \n",
      "\n",
      "normalization...\n",
      "1 10000\n",
      "normalization done\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_08.npz\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_07.npz\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 113, 128, 132)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 113, 137, 141)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_06.npz\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 109, 130, 144)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 109, 139, 154)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_11.npz\n",
      "before: {'spacing': array([0.8370536, 1.5      , 0.8370536]), 'spacing_transposed': array([1.5      , 0.8370536, 0.8370536]), 'data.shape (data is transposed)': (1, 121, 173, 166)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 121, 154, 148)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "before: {'spacing': array([0.8370536, 1.5      , 0.8370536]), 'spacing_transposed': array([1.5      , 0.8370536, 0.8370536]), 'data.shape (data is transposed)': (1, 115, 159, 167)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 115, 142, 149)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "2 10000\n",
      "1 9419\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_10.npz\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_09.npz\n",
      "before: {'spacing': array([1. , 1.5, 1. ]), 'spacing_transposed': array([1.5, 1. , 1. ]), 'data.shape (data is transposed)': (1, 111, 133, 143)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 111, 142, 153)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_12.npz\n",
      "no separate z, order 1\n",
      "before: {'spacing': array([0.8370536, 1.5      , 0.8370536]), 'spacing_transposed': array([1.5      , 0.8370536, 0.8370536]), 'data.shape (data is transposed)': (1, 120, 165, 171)} \n",
      "after:  {'spacing': array([1.5   , 0.9375, 0.9375]), 'data.shape (data is resampled)': (1, 120, 147, 153)} \n",
      "\n",
      "normalization...\n",
      "normalization done\n",
      "1 10000\n",
      "2 10000\n",
      "3 10000\n",
      "saving:  /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D_stage0/IBSR_15.npz\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_plan_and_preprocess -t 975 --verify_dataset_integrity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "- To train - nnUNet_train TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD\n",
    "- To resume - nnUNet_train TRAINER_CLASS_NAME TASK_NAME_OR_ID FOLD -c (just add -c to the training command)\n",
    "- TRAINER_CLASS_NAME - 2d, 3d_fullres, 3d_lowres, 3d_cascade_fullres\n",
    "- Everything will be stored in the results folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\n",
      "If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n",
      "\n",
      "usage: nnUNet_train [-h] [-val] [-c] [-p P] [--use_compressed_data]\n",
      "                    [--deterministic] [--npz] [--find_lr] [--valbest] [--fp32]\n",
      "                    [--val_folder VAL_FOLDER] [--disable_saving]\n",
      "                    [--disable_postprocessing_on_folds]\n",
      "                    [--disable_validation_inference] [--val_disable_overwrite]\n",
      "                    [--disable_next_stage_pred]\n",
      "                    [-pretrained_weights PRETRAINED_WEIGHTS]\n",
      "                    network network_trainer task fold\n",
      "\n",
      "positional arguments:\n",
      "  network\n",
      "  network_trainer\n",
      "  task                  can be task name or task id\n",
      "  fold                  0, 1, ..., 5 or 'all'\n",
      "\n",
      "options:\n",
      "  -h, --help            show this help message and exit\n",
      "  -val, --validation_only\n",
      "                        use this if you want to only run the validation\n",
      "  -c, --continue_training\n",
      "                        use this if you want to continue a training\n",
      "  -p P                  plans identifier. Only change this if you created a\n",
      "                        custom experiment planner\n",
      "  --use_compressed_data\n",
      "                        If you set use_compressed_data, the training cases\n",
      "                        will not be decompressed. Reading compressed data is\n",
      "                        much more CPU and RAM intensive and should only be\n",
      "                        used if you know what you are doing\n",
      "  --deterministic       Makes training deterministic, but reduces training\n",
      "                        speed substantially. I (Fabian) think this is not\n",
      "                        necessary. Deterministic training will make you\n",
      "                        overfit to some random seed. Don't use that.\n",
      "  --npz                 if set then nnUNet will export npz files of predicted\n",
      "                        segmentations in the validation as well. This is\n",
      "                        needed to run the ensembling step so unless you are\n",
      "                        developing nnUNet you should enable this\n",
      "  --find_lr             not used here, just for fun\n",
      "  --valbest             hands off. This is not intended to be used\n",
      "  --fp32                disable mixed precision training and run old school\n",
      "                        fp32\n",
      "  --val_folder VAL_FOLDER\n",
      "                        name of the validation folder. No need to use this for\n",
      "                        most people\n",
      "  --disable_saving      If set nnU-Net will not save any parameter files\n",
      "                        (except a temporary checkpoint that will be removed at\n",
      "                        the end of the training). Useful for development when\n",
      "                        you are only interested in the results and want to\n",
      "                        save some disk space\n",
      "  --disable_postprocessing_on_folds\n",
      "                        Running postprocessing on each fold only makes sense\n",
      "                        when developing with nnU-Net and closely observing the\n",
      "                        model performance on specific configurations. You do\n",
      "                        not need it when applying nnU-Net because the\n",
      "                        postprocessing for this will be determined only once\n",
      "                        all five folds have been trained and\n",
      "                        nnUNet_find_best_configuration is called. Usually\n",
      "                        running postprocessing on each fold is computationally\n",
      "                        cheap, but some users have reported issues with very\n",
      "                        large images. If your images are large (>600x600x600\n",
      "                        voxels) you should consider setting this flag.\n",
      "  --disable_validation_inference\n",
      "                        If set nnU-Net will not run inference on the\n",
      "                        validation set. This is useful if you are only\n",
      "                        interested in the test set results and want to save\n",
      "                        some disk space and time.\n",
      "  --val_disable_overwrite\n",
      "                        Validation does not overwrite existing segmentations\n",
      "  --disable_next_stage_pred\n",
      "                        do not predict next stage\n",
      "  -pretrained_weights PRETRAINED_WEIGHTS\n",
      "                        path to nnU-Net checkpoint file to be used as\n",
      "                        pretrained model (use .model file, for example\n",
      "                        model_final_checkpoint.model). Will only be used when\n",
      "                        actually training. Optional. Beta. Use with caution.\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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_Fast.nnUNetTrainerV2_Fast'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  3\n",
      "modalities:  {0: 'T1'}\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': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2024-01-09 22:21:22.927790: Using splits from existing split file: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/splits_final.pkl\n",
      "2024-01-09 22:21:22.928742: The split file contains 5 splits.\n",
      "2024-01-09 22:21:22.928853: Desired fold for training: 3\n",
      "2024-01-09 22:21:22.928922: This split has 12 training and 3 validation cases.\n",
      "unpacking dataset\n",
      "done\n",
      "2024-01-09 22:21:23.847691: loading checkpoint /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_3/model_final_checkpoint.model train= True\n",
      "2024-01-09 22:21:24.377079: lr: 0.004384\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'instance_norm' is set to train=True. Exporting with train=True.\n",
      "  warnings.warn(\n",
      "2024-01-09 22:21:36.163085: Unable to plot network architecture:\n",
      "2024-01-09 22:21:36.163313: failed to execute ['dot', '-Tpdf', '-O', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_3/network_architecture'], make sure the Graphviz executables are on your systems' PATH\n",
      "2024-01-09 22:21:36.163381: \n",
      "printing the network instead:\n",
      "\n",
      "2024-01-09 22:21:36.163427: 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, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2024-01-09 22:21:36.165310: \n",
      "\n",
      "2024-01-09 22:21:36.167480: \n",
      "epoch:  30\n",
      "2024-01-09 22:29:46.306145: train loss : -0.8646\n",
      "2024-01-09 22:30:18.618167: validation loss: -0.8494\n",
      "2024-01-09 22:30:18.618795: Average global foreground Dice: [0.9173, 0.9461, 0.9369]\n",
      "2024-01-09 22:30:18.618932: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 22:30:19.264531: lr: 0.004186\n",
      "2024-01-09 22:30:19.316816: saving checkpoint...\n",
      "2024-01-09 22:30:19.875711: done, saving took 0.61 seconds\n",
      "2024-01-09 22:30:19.876786: This epoch took 523.709126 s\n",
      "\n",
      "2024-01-09 22:30:19.876867: \n",
      "epoch:  31\n",
      "2024-01-09 22:38:08.097043: train loss : -0.8648\n",
      "2024-01-09 22:38:41.596337: validation loss: -0.8499\n",
      "2024-01-09 22:38:41.596807: Average global foreground Dice: [0.918, 0.9461, 0.9375]\n",
      "2024-01-09 22:38:41.596903: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 22:38:42.224087: lr: 0.003987\n",
      "2024-01-09 22:38:42.243864: saving checkpoint...\n",
      "2024-01-09 22:38:42.868195: done, saving took 0.64 seconds\n",
      "2024-01-09 22:38:42.869091: This epoch took 502.992117 s\n",
      "\n",
      "2024-01-09 22:38:42.869167: \n",
      "epoch:  32\n",
      "2024-01-09 22:46:41.871749: train loss : -0.8658\n",
      "2024-01-09 22:47:15.782876: validation loss: -0.8502\n",
      "2024-01-09 22:47:15.783649: Average global foreground Dice: [0.9164, 0.9467, 0.9379]\n",
      "2024-01-09 22:47:15.783776: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 22:47:16.421962: lr: 0.003787\n",
      "2024-01-09 22:47:16.449232: saving checkpoint...\n",
      "2024-01-09 22:47:19.884496: done, saving took 3.46 seconds\n",
      "2024-01-09 22:47:19.885485: This epoch took 517.016222 s\n",
      "\n",
      "2024-01-09 22:47:19.885649: \n",
      "epoch:  33\n",
      "2024-01-09 22:55:22.352751: train loss : -0.8655\n",
      "2024-01-09 22:55:56.390148: validation loss: -0.8522\n",
      "2024-01-09 22:55:56.390604: Average global foreground Dice: [0.9169, 0.9476, 0.9387]\n",
      "2024-01-09 22:55:56.390694: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 22:55:57.015091: lr: 0.003586\n",
      "2024-01-09 22:55:57.041486: saving checkpoint...\n",
      "2024-01-09 22:56:02.149628: done, saving took 5.13 seconds\n",
      "2024-01-09 22:56:02.154772: This epoch took 522.269033 s\n",
      "\n",
      "2024-01-09 22:56:02.154910: \n",
      "epoch:  34\n",
      "2024-01-09 23:04:03.829441: train loss : -0.8672\n",
      "2024-01-09 23:04:37.920543: validation loss: -0.8516\n",
      "2024-01-09 23:04:37.921141: Average global foreground Dice: [0.9173, 0.9471, 0.9389]\n",
      "2024-01-09 23:04:37.921273: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:04:38.615293: lr: 0.003384\n",
      "2024-01-09 23:04:38.645613: saving checkpoint...\n",
      "2024-01-09 23:04:44.945171: done, saving took 6.33 seconds\n",
      "2024-01-09 23:04:44.946523: This epoch took 522.791449 s\n",
      "\n",
      "2024-01-09 23:04:44.946660: \n",
      "epoch:  35\n",
      "2024-01-09 23:12:48.891301: train loss : -0.8689\n",
      "2024-01-09 23:13:23.172318: validation loss: -0.8542\n",
      "2024-01-09 23:13:23.173111: Average global foreground Dice: [0.9177, 0.9487, 0.9402]\n",
      "2024-01-09 23:13:23.173256: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:13:23.894991: lr: 0.00318\n",
      "2024-01-09 23:13:23.924835: saving checkpoint...\n",
      "2024-01-09 23:13:24.512333: done, saving took 0.62 seconds\n",
      "2024-01-09 23:13:24.513121: This epoch took 519.566371 s\n",
      "\n",
      "2024-01-09 23:13:24.513201: \n",
      "epoch:  36\n",
      "2024-01-09 23:21:29.918982: train loss : -0.8684\n",
      "2024-01-09 23:22:04.231899: validation loss: -0.8531\n",
      "2024-01-09 23:22:04.232493: Average global foreground Dice: [0.9184, 0.9475, 0.9394]\n",
      "2024-01-09 23:22:04.232626: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:22:04.977016: lr: 0.002975\n",
      "2024-01-09 23:22:05.130094: saving checkpoint...\n",
      "2024-01-09 23:22:05.713934: done, saving took 0.74 seconds\n",
      "2024-01-09 23:22:05.714963: This epoch took 521.201679 s\n",
      "\n",
      "2024-01-09 23:22:05.715048: \n",
      "epoch:  37\n",
      "2024-01-09 23:30:11.737826: train loss : -0.8694\n",
      "2024-01-09 23:30:46.123523: validation loss: -0.8548\n",
      "2024-01-09 23:30:46.124104: Average global foreground Dice: [0.9203, 0.9482, 0.9398]\n",
      "2024-01-09 23:30:46.124209: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:30:46.704125: lr: 0.002768\n",
      "2024-01-09 23:30:46.717198: saving checkpoint...\n",
      "2024-01-09 23:30:47.295426: done, saving took 0.59 seconds\n",
      "2024-01-09 23:30:47.296089: This epoch took 521.580932 s\n",
      "\n",
      "2024-01-09 23:30:47.296148: \n",
      "epoch:  38\n",
      "2024-01-09 23:38:53.995415: train loss : -0.8695\n",
      "2024-01-09 23:39:28.259849: validation loss: -0.8540\n",
      "2024-01-09 23:39:28.260474: Average global foreground Dice: [0.9193, 0.9481, 0.9393]\n",
      "2024-01-09 23:39:28.260678: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:39:28.925708: lr: 0.00256\n",
      "2024-01-09 23:39:28.940839: saving checkpoint...\n",
      "2024-01-09 23:39:29.563561: done, saving took 0.64 seconds\n",
      "2024-01-09 23:39:29.564757: This epoch took 522.268519 s\n",
      "\n",
      "2024-01-09 23:39:29.564851: \n",
      "epoch:  39\n",
      "2024-01-09 23:47:36.324253: train loss : -0.8707\n",
      "2024-01-09 23:48:10.800093: validation loss: -0.8545\n",
      "2024-01-09 23:48:10.800614: Average global foreground Dice: [0.9213, 0.9479, 0.9394]\n",
      "2024-01-09 23:48:10.800719: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:48:11.563337: lr: 0.002349\n",
      "2024-01-09 23:48:11.596586: saving checkpoint...\n",
      "2024-01-09 23:48:12.269850: done, saving took 0.71 seconds\n",
      "2024-01-09 23:48:12.271215: This epoch took 522.706264 s\n",
      "\n",
      "2024-01-09 23:48:12.271369: \n",
      "epoch:  40\n",
      "2024-01-09 23:56:20.128813: train loss : -0.8715\n",
      "2024-01-09 23:56:54.562637: validation loss: -0.8535\n",
      "2024-01-09 23:56:54.563453: Average global foreground Dice: [0.9174, 0.9474, 0.9397]\n",
      "2024-01-09 23:56:54.563604: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-09 23:56:55.253714: lr: 0.002137\n",
      "2024-01-09 23:56:55.288455: saving checkpoint...\n",
      "2024-01-09 23:56:55.898985: done, saving took 0.65 seconds\n",
      "2024-01-09 23:56:55.900147: This epoch took 523.628703 s\n",
      "\n",
      "2024-01-09 23:56:55.900252: \n",
      "epoch:  41\n",
      "2024-01-10 00:05:02.366837: train loss : -0.8718\n",
      "2024-01-10 00:05:36.809063: validation loss: -0.8547\n",
      "2024-01-10 00:05:36.809610: Average global foreground Dice: [0.9191, 0.9487, 0.9398]\n",
      "2024-01-10 00:05:36.809714: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:05:37.440432: lr: 0.001922\n",
      "2024-01-10 00:05:37.473625: saving checkpoint...\n",
      "2024-01-10 00:05:42.623338: done, saving took 5.18 seconds\n",
      "2024-01-10 00:05:42.628029: This epoch took 526.727651 s\n",
      "\n",
      "2024-01-10 00:05:42.628104: \n",
      "epoch:  42\n",
      "2024-01-10 00:13:48.675941: train loss : -0.8724\n",
      "2024-01-10 00:14:23.041285: validation loss: -0.8527\n",
      "2024-01-10 00:14:23.042427: Average global foreground Dice: [0.9168, 0.9478, 0.9398]\n",
      "2024-01-10 00:14:23.042643: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:14:23.725672: lr: 0.001704\n",
      "2024-01-10 00:14:23.756595: saving checkpoint...\n",
      "2024-01-10 00:14:29.143222: done, saving took 5.42 seconds\n",
      "2024-01-10 00:14:29.148918: This epoch took 526.520758 s\n",
      "\n",
      "2024-01-10 00:14:29.149065: \n",
      "epoch:  43\n",
      "2024-01-10 00:22:34.805856: train loss : -0.8724\n",
      "2024-01-10 00:23:09.038068: validation loss: -0.8582\n",
      "2024-01-10 00:23:09.038608: Average global foreground Dice: [0.9227, 0.9494, 0.9407]\n",
      "2024-01-10 00:23:09.038745: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:23:09.775447: lr: 0.001483\n",
      "2024-01-10 00:23:09.810817: saving checkpoint...\n",
      "2024-01-10 00:23:10.352330: done, saving took 0.58 seconds\n",
      "2024-01-10 00:23:10.353229: This epoch took 521.204017 s\n",
      "\n",
      "2024-01-10 00:23:10.353299: \n",
      "epoch:  44\n",
      "2024-01-10 00:31:16.843240: train loss : -0.8735\n",
      "2024-01-10 00:31:51.130102: validation loss: -0.8542\n",
      "2024-01-10 00:31:51.130711: Average global foreground Dice: [0.9193, 0.9481, 0.9399]\n",
      "2024-01-10 00:31:51.130813: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:31:51.776590: lr: 0.001259\n",
      "2024-01-10 00:31:51.809678: saving checkpoint...\n",
      "2024-01-10 00:31:52.372171: done, saving took 0.60 seconds\n",
      "2024-01-10 00:31:52.378464: This epoch took 522.025068 s\n",
      "\n",
      "2024-01-10 00:31:52.378550: \n",
      "epoch:  45\n",
      "2024-01-10 00:39:58.536004: train loss : -0.8735\n",
      "2024-01-10 00:40:32.887277: validation loss: -0.8561\n",
      "2024-01-10 00:40:32.887887: Average global foreground Dice: [0.9209, 0.9484, 0.9395]\n",
      "2024-01-10 00:40:32.888001: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:40:33.612920: lr: 0.00103\n",
      "2024-01-10 00:40:33.646879: saving checkpoint...\n",
      "2024-01-10 00:40:34.377820: done, saving took 0.76 seconds\n",
      "2024-01-10 00:40:34.382703: This epoch took 522.004098 s\n",
      "\n",
      "2024-01-10 00:40:34.382794: \n",
      "epoch:  46\n",
      "2024-01-10 00:48:41.105376: train loss : -0.8740\n",
      "2024-01-10 00:49:15.455538: validation loss: -0.8556\n",
      "2024-01-10 00:49:15.456047: Average global foreground Dice: [0.9188, 0.9485, 0.9402]\n",
      "2024-01-10 00:49:15.456157: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:49:16.301987: lr: 0.000795\n",
      "2024-01-10 00:49:16.323066: saving checkpoint...\n",
      "2024-01-10 00:49:16.918966: done, saving took 0.62 seconds\n",
      "2024-01-10 00:49:16.919749: This epoch took 522.536891 s\n",
      "\n",
      "2024-01-10 00:49:16.919805: \n",
      "epoch:  47\n",
      "2024-01-10 00:57:22.403777: train loss : -0.8747\n",
      "2024-01-10 00:57:56.728198: validation loss: -0.8556\n",
      "2024-01-10 00:57:56.728974: Average global foreground Dice: [0.9208, 0.9486, 0.9403]\n",
      "2024-01-10 00:57:56.729176: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 00:57:57.391812: lr: 0.000552\n",
      "2024-01-10 00:57:57.417886: saving checkpoint...\n",
      "2024-01-10 00:57:59.903913: done, saving took 2.51 seconds\n",
      "2024-01-10 00:57:59.905319: This epoch took 522.985458 s\n",
      "\n",
      "2024-01-10 00:57:59.905455: \n",
      "epoch:  48\n",
      "2024-01-10 01:06:06.092673: train loss : -0.8745\n",
      "2024-01-10 01:06:40.599077: validation loss: -0.8569\n",
      "2024-01-10 01:06:40.599523: Average global foreground Dice: [0.9213, 0.9493, 0.9404]\n",
      "2024-01-10 01:06:40.599622: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 01:06:41.203792: lr: 0.000296\n",
      "2024-01-10 01:06:41.222646: saving checkpoint...\n",
      "2024-01-10 01:06:45.461454: done, saving took 4.26 seconds\n",
      "2024-01-10 01:06:45.463036: This epoch took 525.557481 s\n",
      "\n",
      "2024-01-10 01:06:45.463107: \n",
      "epoch:  49\n",
      "2024-01-10 01:14:50.265994: train loss : -0.8753\n",
      "2024-01-10 01:15:24.594673: validation loss: -0.8562\n",
      "2024-01-10 01:15:24.595249: Average global foreground Dice: [0.9208, 0.9491, 0.9406]\n",
      "2024-01-10 01:15:24.595370: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 01:15:25.355265: lr: 0.0\n",
      "2024-01-10 01:15:25.355478: saving scheduled checkpoint file...\n",
      "2024-01-10 01:15:25.384491: saving checkpoint...\n",
      "2024-01-10 01:15:25.685787: done, saving took 0.33 seconds\n",
      "2024-01-10 01:15:25.686597: done\n",
      "2024-01-10 01:15:25.708367: saving checkpoint...\n",
      "2024-01-10 01:15:26.678164: done, saving took 0.99 seconds\n",
      "2024-01-10 01:15:26.679212: This epoch took 521.216002 s\n",
      "\n",
      "2024-01-10 01:15:26.700827: saving checkpoint...\n",
      "2024-01-10 01:15:27.407454: done, saving took 0.73 seconds\n",
      "IBSR_05 (2, 119, 142, 136)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "IBSR_10 (2, 121, 154, 148)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "IBSR_14 (2, 116, 144, 146)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "2024-01-10 01:15:54.531877: finished prediction\n",
      "2024-01-10 01:15:54.532190: evaluation of raw predictions\n",
      "2024-01-10 01:15:55.699827: determining postprocessing\n",
      "Foreground vs background\n",
      "before: 0.9394573143585001\n",
      "after:  0.9394562735792679\n",
      "1\n",
      "before: 0.9255760930772418\n",
      "after:  0.859786697136515\n",
      "2\n",
      "before: 0.9494358616631903\n",
      "after:  0.9494606705525513\n",
      "Removing all but the largest region for class 2 improved results!\n",
      "min_valid_object_sizes None\n",
      "3\n",
      "before: 0.9433599883350685\n",
      "after:  0.9104060076391969\n",
      "done\n",
      "for which classes:\n",
      "[2]\n",
      "min_object_sizes\n",
      "None\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train 2d nnUNetTrainerV2_Fast Task975_BrainSegmentation 3 --npz -c "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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_Fast.nnUNetTrainerV2_Fast'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  3\n",
      "modalities:  {0: 'T1'}\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': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2024-01-10 01:16:08.639095: Using splits from existing split file: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/splits_final.pkl\n",
      "2024-01-10 01:16:08.639482: The split file contains 5 splits.\n",
      "2024-01-10 01:16:08.639561: Desired fold for training: 0\n",
      "2024-01-10 01:16:08.639623: This split has 12 training and 3 validation cases.\n",
      "unpacking dataset\n",
      "done\n",
      "2024-01-10 01:16:09.675237: loading checkpoint /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_0/model_final_checkpoint.model train= True\n",
      "2024-01-10 01:16:10.170271: lr: 0.004384\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'instance_norm' is set to train=True. Exporting with train=True.\n",
      "  warnings.warn(\n",
      "2024-01-10 01:16:23.360611: Unable to plot network architecture:\n",
      "2024-01-10 01:16:23.360815: failed to execute ['dot', '-Tpdf', '-O', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_0/network_architecture'], make sure the Graphviz executables are on your systems' PATH\n",
      "2024-01-10 01:16:23.360925: \n",
      "printing the network instead:\n",
      "\n",
      "2024-01-10 01:16:23.361010: 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, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2024-01-10 01:16:23.364809: \n",
      "\n",
      "2024-01-10 01:16:23.367413: \n",
      "epoch:  30\n",
      "2024-01-10 01:25:36.319128: train loss : -0.8714\n",
      "2024-01-10 01:26:10.465770: validation loss: -0.8423\n",
      "2024-01-10 01:26:10.466398: Average global foreground Dice: [0.8983, 0.9421, 0.9377]\n",
      "2024-01-10 01:26:10.466547: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 01:26:11.180807: lr: 0.004186\n",
      "2024-01-10 01:26:11.237871: saving checkpoint...\n",
      "2024-01-10 01:26:11.769665: done, saving took 0.59 seconds\n",
      "2024-01-10 01:26:11.770466: This epoch took 588.402814 s\n",
      "\n",
      "2024-01-10 01:26:11.770592: \n",
      "epoch:  31\n",
      "2024-01-10 01:34:15.068971: train loss : -0.8710\n",
      "2024-01-10 01:34:49.154904: validation loss: -0.8418\n",
      "2024-01-10 01:34:49.155614: Average global foreground Dice: [0.893, 0.9427, 0.9387]\n",
      "2024-01-10 01:34:49.155760: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 01:34:49.786760: lr: 0.003987\n",
      "2024-01-10 01:34:49.786949: This epoch took 518.016207 s\n",
      "\n",
      "2024-01-10 01:34:49.787035: \n",
      "epoch:  32\n",
      "2024-01-10 01:42:53.072135: train loss : -0.8725\n",
      "2024-01-10 01:43:27.192431: validation loss: -0.8436\n",
      "2024-01-10 01:43:27.192993: Average global foreground Dice: [0.8971, 0.9435, 0.9388]\n",
      "2024-01-10 01:43:27.193116: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 01:43:27.907876: lr: 0.003787\n",
      "2024-01-10 01:43:27.908097: This epoch took 518.120976 s\n",
      "\n",
      "2024-01-10 01:43:27.908233: \n",
      "epoch:  33\n",
      "2024-01-10 01:51:31.719904: train loss : -0.8734\n",
      "2024-01-10 01:52:05.924855: validation loss: -0.8417\n",
      "2024-01-10 01:52:05.925368: Average global foreground Dice: [0.893, 0.9425, 0.9382]\n",
      "2024-01-10 01:52:05.925474: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 01:52:06.596749: lr: 0.003586\n",
      "2024-01-10 01:52:06.597032: This epoch took 518.688669 s\n",
      "\n",
      "2024-01-10 01:52:06.597136: \n",
      "epoch:  34\n",
      "2024-01-10 02:00:10.034422: train loss : -0.8745\n",
      "2024-01-10 02:00:43.998739: validation loss: -0.8399\n",
      "2024-01-10 02:00:43.999276: Average global foreground Dice: [0.8958, 0.9421, 0.9372]\n",
      "2024-01-10 02:00:43.999386: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:00:44.640524: lr: 0.003384\n",
      "2024-01-10 02:00:44.640704: This epoch took 518.043463 s\n",
      "\n",
      "2024-01-10 02:00:44.640800: \n",
      "epoch:  35\n",
      "2024-01-10 02:08:48.359090: train loss : -0.8744\n",
      "2024-01-10 02:09:22.335692: validation loss: -0.8406\n",
      "2024-01-10 02:09:22.336223: Average global foreground Dice: [0.8917, 0.9428, 0.9381]\n",
      "2024-01-10 02:09:22.336318: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:09:22.902935: lr: 0.00318\n",
      "2024-01-10 02:09:22.903160: This epoch took 518.262258 s\n",
      "\n",
      "2024-01-10 02:09:22.903263: \n",
      "epoch:  36\n",
      "2024-01-10 02:17:26.592593: train loss : -0.8757\n",
      "2024-01-10 02:18:00.715319: validation loss: -0.8417\n",
      "2024-01-10 02:18:00.715938: Average global foreground Dice: [0.895, 0.9426, 0.938]\n",
      "2024-01-10 02:18:00.716051: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:18:01.548153: lr: 0.002975\n",
      "2024-01-10 02:18:01.548380: This epoch took 518.645015 s\n",
      "\n",
      "2024-01-10 02:18:01.548475: \n",
      "epoch:  37\n",
      "2024-01-10 02:26:04.517847: train loss : -0.8761\n",
      "2024-01-10 02:26:38.544115: validation loss: -0.8449\n",
      "2024-01-10 02:26:38.544627: Average global foreground Dice: [0.8985, 0.9435, 0.9379]\n",
      "2024-01-10 02:26:38.544746: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:26:39.285104: lr: 0.002768\n",
      "2024-01-10 02:26:39.285307: This epoch took 517.736741 s\n",
      "\n",
      "2024-01-10 02:26:39.285404: \n",
      "epoch:  38\n",
      "2024-01-10 02:34:41.925389: train loss : -0.8780\n",
      "2024-01-10 02:35:15.967471: validation loss: -0.8436\n",
      "2024-01-10 02:35:15.968062: Average global foreground Dice: [0.8956, 0.9428, 0.9386]\n",
      "2024-01-10 02:35:15.968177: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:35:16.716708: lr: 0.00256\n",
      "2024-01-10 02:35:16.716904: This epoch took 517.431407 s\n",
      "\n",
      "2024-01-10 02:35:16.716999: \n",
      "epoch:  39\n",
      "2024-01-10 02:43:20.080662: train loss : -0.8779\n",
      "2024-01-10 02:43:54.141653: validation loss: -0.8411\n",
      "2024-01-10 02:43:54.142167: Average global foreground Dice: [0.8946, 0.9429, 0.938]\n",
      "2024-01-10 02:43:54.142279: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:43:54.816778: lr: 0.002349\n",
      "2024-01-10 02:43:54.816965: This epoch took 518.099874 s\n",
      "\n",
      "2024-01-10 02:43:54.817049: \n",
      "epoch:  40\n",
      "2024-01-10 02:51:57.613787: train loss : -0.8782\n",
      "2024-01-10 02:52:31.584868: validation loss: -0.8402\n",
      "2024-01-10 02:52:31.585609: Average global foreground Dice: [0.8942, 0.9426, 0.9379]\n",
      "2024-01-10 02:52:31.585760: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 02:52:32.308676: lr: 0.002137\n",
      "2024-01-10 02:52:32.308948: This epoch took 517.491815 s\n",
      "\n",
      "2024-01-10 02:52:32.309075: \n",
      "epoch:  41\n",
      "2024-01-10 03:00:34.444721: train loss : -0.8790\n",
      "2024-01-10 03:01:08.501946: validation loss: -0.8412\n",
      "2024-01-10 03:01:08.502556: Average global foreground Dice: [0.8939, 0.9427, 0.9385]\n",
      "2024-01-10 03:01:08.502678: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:01:09.213115: lr: 0.001922\n",
      "2024-01-10 03:01:09.213338: This epoch took 516.904134 s\n",
      "\n",
      "2024-01-10 03:01:09.213440: \n",
      "epoch:  42\n",
      "2024-01-10 03:09:11.315728: train loss : -0.8810\n",
      "2024-01-10 03:09:45.416823: validation loss: -0.8410\n",
      "2024-01-10 03:09:45.417260: Average global foreground Dice: [0.8929, 0.9428, 0.9386]\n",
      "2024-01-10 03:09:45.417359: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:09:46.004506: lr: 0.001704\n",
      "2024-01-10 03:09:46.004660: This epoch took 516.791124 s\n",
      "\n",
      "2024-01-10 03:09:46.004731: \n",
      "epoch:  43\n",
      "2024-01-10 03:17:47.832545: train loss : -0.8807\n",
      "2024-01-10 03:18:21.728669: validation loss: -0.8415\n",
      "2024-01-10 03:18:21.729163: Average global foreground Dice: [0.895, 0.9429, 0.9385]\n",
      "2024-01-10 03:18:21.729265: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:18:22.454201: lr: 0.001483\n",
      "2024-01-10 03:18:22.454405: This epoch took 516.449599 s\n",
      "\n",
      "2024-01-10 03:18:22.454512: \n",
      "epoch:  44\n",
      "2024-01-10 03:26:23.923215: train loss : -0.8812\n",
      "2024-01-10 03:26:57.928068: validation loss: -0.8408\n",
      "2024-01-10 03:26:57.928641: Average global foreground Dice: [0.8905, 0.9426, 0.9382]\n",
      "2024-01-10 03:26:57.928765: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:26:58.569901: lr: 0.001259\n",
      "2024-01-10 03:26:58.570102: This epoch took 516.115475 s\n",
      "\n",
      "2024-01-10 03:26:58.570188: \n",
      "epoch:  45\n",
      "2024-01-10 03:34:59.159300: train loss : -0.8817\n",
      "2024-01-10 03:35:33.027869: validation loss: -0.8410\n",
      "2024-01-10 03:35:33.028452: Average global foreground Dice: [0.8979, 0.9425, 0.9377]\n",
      "2024-01-10 03:35:33.028584: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:35:33.666721: lr: 0.00103\n",
      "2024-01-10 03:35:33.666917: This epoch took 515.096645 s\n",
      "\n",
      "2024-01-10 03:35:33.667020: \n",
      "epoch:  46\n",
      "2024-01-10 03:43:35.836523: train loss : -0.8814\n",
      "2024-01-10 03:44:09.736627: validation loss: -0.8408\n",
      "2024-01-10 03:44:09.737131: Average global foreground Dice: [0.8951, 0.942, 0.9375]\n",
      "2024-01-10 03:44:09.737235: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:44:10.516066: lr: 0.000795\n",
      "2024-01-10 03:44:10.516258: This epoch took 516.849132 s\n",
      "\n",
      "2024-01-10 03:44:10.516345: \n",
      "epoch:  47\n",
      "2024-01-10 03:52:11.233033: train loss : -0.8827\n",
      "2024-01-10 03:52:45.200080: validation loss: -0.8416\n",
      "2024-01-10 03:52:45.200763: Average global foreground Dice: [0.8949, 0.9428, 0.9378]\n",
      "2024-01-10 03:52:45.200897: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 03:52:45.912708: lr: 0.000552\n",
      "2024-01-10 03:52:45.912913: This epoch took 515.396473 s\n",
      "\n",
      "2024-01-10 03:52:45.913006: \n",
      "epoch:  48\n",
      "2024-01-10 04:00:47.528296: train loss : -0.8828\n",
      "2024-01-10 04:01:21.510693: validation loss: -0.8391\n",
      "2024-01-10 04:01:21.511411: Average global foreground Dice: [0.8906, 0.9427, 0.9381]\n",
      "2024-01-10 04:01:21.511691: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:01:22.071691: lr: 0.000296\n",
      "2024-01-10 04:01:22.071861: This epoch took 516.158765 s\n",
      "\n",
      "2024-01-10 04:01:22.071934: \n",
      "epoch:  49\n",
      "2024-01-10 04:09:23.641264: train loss : -0.8829\n",
      "2024-01-10 04:09:57.677573: validation loss: -0.8387\n",
      "2024-01-10 04:09:57.678137: Average global foreground Dice: [0.8927, 0.9424, 0.9375]\n",
      "2024-01-10 04:09:57.678258: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:09:58.387386: lr: 0.0\n",
      "2024-01-10 04:09:58.387649: saving scheduled checkpoint file...\n",
      "2024-01-10 04:09:58.405320: saving checkpoint...\n",
      "2024-01-10 04:09:58.659909: done, saving took 0.27 seconds\n",
      "2024-01-10 04:09:58.661163: done\n",
      "2024-01-10 04:09:58.661307: This epoch took 516.589278 s\n",
      "\n",
      "2024-01-10 04:09:58.677485: saving checkpoint...\n",
      "2024-01-10 04:09:59.286308: done, saving took 0.62 seconds\n",
      "IBSR_01 (2, 111, 131, 143)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "IBSR_08 (2, 115, 147, 147)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "IBSR_12 (2, 111, 142, 153)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "2024-01-10 04:10:25.897124: finished prediction\n",
      "2024-01-10 04:10:25.897438: evaluation of raw predictions\n",
      "2024-01-10 04:10:26.956700: determining postprocessing\n",
      "Foreground vs background\n",
      "before: 0.9278905332430031\n",
      "after:  0.9278927647147052\n",
      "Removing all but the largest foreground region improved results!\n",
      "for_which_classes [1, 2, 3]\n",
      "min_valid_object_sizes None\n",
      "1\n",
      "before: 0.8926609633102552\n",
      "after:  0.8463875594407897\n",
      "2\n",
      "before: 0.9476809174695769\n",
      "after:  0.9476910976641285\n",
      "Removing all but the largest region for class 2 improved results!\n",
      "min_valid_object_sizes None\n",
      "3\n",
      "before: 0.9433364133642833\n",
      "after:  0.8965243777877673\n",
      "done\n",
      "for which classes:\n",
      "[[1, 2, 3], 2]\n",
      "min_object_sizes\n",
      "None\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train 2d nnUNetTrainerV2_Fast Task975_BrainSegmentation 0 --npz -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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_Fast.nnUNetTrainerV2_Fast'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  3\n",
      "modalities:  {0: 'T1'}\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': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2024-01-10 04:10:40.290094: Using splits from existing split file: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/splits_final.pkl\n",
      "2024-01-10 04:10:40.290563: The split file contains 5 splits.\n",
      "2024-01-10 04:10:40.290631: Desired fold for training: 1\n",
      "2024-01-10 04:10:40.290691: This split has 12 training and 3 validation cases.\n",
      "unpacking dataset\n",
      "done\n",
      "2024-01-10 04:10:41.212771: loading checkpoint /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_1/model_final_checkpoint.model train= True\n",
      "2024-01-10 04:10:41.725018: lr: 0.004384\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'instance_norm' is set to train=True. Exporting with train=True.\n",
      "  warnings.warn(\n",
      "2024-01-10 04:10:54.630715: Unable to plot network architecture:\n",
      "2024-01-10 04:10:54.631077: failed to execute ['dot', '-Tpdf', '-O', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_1/network_architecture'], make sure the Graphviz executables are on your systems' PATH\n",
      "2024-01-10 04:10:54.631258: \n",
      "printing the network instead:\n",
      "\n",
      "2024-01-10 04:10:54.631395: 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, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2024-01-10 04:10:54.635693: \n",
      "\n",
      "2024-01-10 04:10:54.638496: \n",
      "epoch:  30\n",
      "2024-01-10 04:19:58.522310: train loss : -0.8692\n",
      "2024-01-10 04:20:32.253369: validation loss: -0.8423\n",
      "2024-01-10 04:20:32.253876: Average global foreground Dice: [0.9011, 0.9431, 0.9367]\n",
      "2024-01-10 04:20:32.253999: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:20:33.033213: lr: 0.004186\n",
      "2024-01-10 04:20:33.094777: saving checkpoint...\n",
      "2024-01-10 04:20:33.691902: done, saving took 0.66 seconds\n",
      "2024-01-10 04:20:33.692752: This epoch took 579.054006 s\n",
      "\n",
      "2024-01-10 04:20:33.692808: \n",
      "epoch:  31\n",
      "2024-01-10 04:28:28.661045: train loss : -0.8698\n",
      "2024-01-10 04:29:02.436571: validation loss: -0.8434\n",
      "2024-01-10 04:29:02.437234: Average global foreground Dice: [0.8969, 0.9446, 0.9374]\n",
      "2024-01-10 04:29:02.437351: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:29:03.058812: lr: 0.003987\n",
      "2024-01-10 04:29:03.058989: This epoch took 509.366092 s\n",
      "\n",
      "2024-01-10 04:29:03.059070: \n",
      "epoch:  32\n",
      "2024-01-10 04:36:58.062344: train loss : -0.8706\n",
      "2024-01-10 04:37:31.799182: validation loss: -0.8418\n",
      "2024-01-10 04:37:31.799911: Average global foreground Dice: [0.8986, 0.9434, 0.9369]\n",
      "2024-01-10 04:37:31.800096: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:37:32.497765: lr: 0.003787\n",
      "2024-01-10 04:37:32.498004: This epoch took 509.438851 s\n",
      "\n",
      "2024-01-10 04:37:32.498152: \n",
      "epoch:  33\n",
      "2024-01-10 04:45:26.887290: train loss : -0.8712\n",
      "2024-01-10 04:46:00.855738: validation loss: -0.8439\n",
      "2024-01-10 04:46:00.856251: Average global foreground Dice: [0.9002, 0.9448, 0.9368]\n",
      "2024-01-10 04:46:00.856353: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:46:01.503885: lr: 0.003586\n",
      "2024-01-10 04:46:01.504069: This epoch took 509.005783 s\n",
      "\n",
      "2024-01-10 04:46:01.504153: \n",
      "epoch:  34\n",
      "2024-01-10 04:53:57.571123: train loss : -0.8717\n",
      "2024-01-10 04:54:31.426591: validation loss: -0.8454\n",
      "2024-01-10 04:54:31.427034: Average global foreground Dice: [0.9007, 0.9444, 0.9368]\n",
      "2024-01-10 04:54:31.427124: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 04:54:32.007324: lr: 0.003384\n",
      "2024-01-10 04:54:32.007504: This epoch took 510.503269 s\n",
      "\n",
      "2024-01-10 04:54:32.007601: \n",
      "epoch:  35\n",
      "2024-01-10 05:02:27.086870: train loss : -0.8711\n",
      "2024-01-10 05:03:00.874886: validation loss: -0.8449\n",
      "2024-01-10 05:03:00.875478: Average global foreground Dice: [0.9029, 0.9443, 0.9371]\n",
      "2024-01-10 05:03:00.875590: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:03:01.617803: lr: 0.00318\n",
      "2024-01-10 05:03:01.641567: saving checkpoint...\n",
      "2024-01-10 05:03:02.267874: done, saving took 0.65 seconds\n",
      "2024-01-10 05:03:02.268553: This epoch took 510.260861 s\n",
      "\n",
      "2024-01-10 05:03:02.268627: \n",
      "epoch:  36\n",
      "2024-01-10 05:10:57.087027: train loss : -0.8737\n",
      "2024-01-10 05:11:30.772422: validation loss: -0.8441\n",
      "2024-01-10 05:11:30.772991: Average global foreground Dice: [0.8994, 0.9447, 0.9377]\n",
      "2024-01-10 05:11:30.773113: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:11:31.441505: lr: 0.002975\n",
      "2024-01-10 05:11:31.469583: saving checkpoint...\n",
      "2024-01-10 05:11:32.402117: done, saving took 0.96 seconds\n",
      "2024-01-10 05:11:32.407500: This epoch took 510.138760 s\n",
      "\n",
      "2024-01-10 05:11:32.407604: \n",
      "epoch:  37\n",
      "2024-01-10 05:19:27.492115: train loss : -0.8730\n",
      "2024-01-10 05:20:01.273673: validation loss: -0.8433\n",
      "2024-01-10 05:20:01.274508: Average global foreground Dice: [0.8989, 0.945, 0.9368]\n",
      "2024-01-10 05:20:01.274847: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:20:01.853724: lr: 0.002768\n",
      "2024-01-10 05:20:01.853878: This epoch took 509.446203 s\n",
      "\n",
      "2024-01-10 05:20:01.853953: \n",
      "epoch:  38\n",
      "2024-01-10 05:27:57.004662: train loss : -0.8741\n",
      "2024-01-10 05:28:30.836854: validation loss: -0.8474\n",
      "2024-01-10 05:28:30.837346: Average global foreground Dice: [0.9008, 0.9459, 0.9383]\n",
      "2024-01-10 05:28:30.837473: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:28:31.477852: lr: 0.00256\n",
      "2024-01-10 05:28:31.507393: saving checkpoint...\n",
      "2024-01-10 05:28:32.095640: done, saving took 0.62 seconds\n",
      "2024-01-10 05:28:32.097034: This epoch took 510.242973 s\n",
      "\n",
      "2024-01-10 05:28:32.097157: \n",
      "epoch:  39\n",
      "2024-01-10 05:36:26.995050: train loss : -0.8752\n",
      "2024-01-10 05:37:00.767966: validation loss: -0.8447\n",
      "2024-01-10 05:37:00.768505: Average global foreground Dice: [0.8969, 0.9454, 0.9381]\n",
      "2024-01-10 05:37:00.768620: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:37:01.452703: lr: 0.002349\n",
      "2024-01-10 05:37:01.452899: This epoch took 509.355600 s\n",
      "\n",
      "2024-01-10 05:37:01.452994: \n",
      "epoch:  40\n",
      "2024-01-10 05:44:55.566190: train loss : -0.8754\n",
      "2024-01-10 05:45:29.296149: validation loss: -0.8452\n",
      "2024-01-10 05:45:29.296685: Average global foreground Dice: [0.8989, 0.9458, 0.9386]\n",
      "2024-01-10 05:45:29.296803: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:45:29.984368: lr: 0.002137\n",
      "2024-01-10 05:45:30.014799: saving checkpoint...\n",
      "2024-01-10 05:45:30.714543: done, saving took 0.73 seconds\n",
      "2024-01-10 05:45:30.715286: This epoch took 509.262207 s\n",
      "\n",
      "2024-01-10 05:45:30.715340: \n",
      "epoch:  41\n",
      "2024-01-10 05:53:24.229903: train loss : -0.8763\n",
      "2024-01-10 05:53:57.868480: validation loss: -0.8446\n",
      "2024-01-10 05:53:57.868985: Average global foreground Dice: [0.8981, 0.9456, 0.9385]\n",
      "2024-01-10 05:53:57.869100: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 05:53:58.491037: lr: 0.001922\n",
      "2024-01-10 05:53:58.516324: saving checkpoint...\n",
      "2024-01-10 05:53:59.106911: done, saving took 0.62 seconds\n",
      "2024-01-10 05:53:59.107678: This epoch took 508.392267 s\n",
      "\n",
      "2024-01-10 05:53:59.107757: \n",
      "epoch:  42\n",
      "2024-01-10 06:01:52.916255: train loss : -0.8761\n",
      "2024-01-10 06:02:26.580656: validation loss: -0.8442\n",
      "2024-01-10 06:02:26.581209: Average global foreground Dice: [0.9001, 0.9451, 0.9379]\n",
      "2024-01-10 06:02:26.581318: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:02:27.227663: lr: 0.001704\n",
      "2024-01-10 06:02:27.249085: saving checkpoint...\n",
      "2024-01-10 06:02:27.831424: done, saving took 0.60 seconds\n",
      "2024-01-10 06:02:27.832581: This epoch took 508.724684 s\n",
      "\n",
      "2024-01-10 06:02:27.832674: \n",
      "epoch:  43\n",
      "2024-01-10 06:10:21.605208: train loss : -0.8769\n",
      "2024-01-10 06:10:55.284287: validation loss: -0.8448\n",
      "2024-01-10 06:10:55.284934: Average global foreground Dice: [0.8999, 0.9456, 0.9377]\n",
      "2024-01-10 06:10:55.285103: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:10:55.905706: lr: 0.001483\n",
      "2024-01-10 06:10:55.930586: saving checkpoint...\n",
      "2024-01-10 06:10:56.566337: done, saving took 0.66 seconds\n",
      "2024-01-10 06:10:56.567361: This epoch took 508.734624 s\n",
      "\n",
      "2024-01-10 06:10:56.567472: \n",
      "epoch:  44\n",
      "2024-01-10 06:18:49.852199: train loss : -0.8764\n",
      "2024-01-10 06:19:23.390183: validation loss: -0.8462\n",
      "2024-01-10 06:19:23.391131: Average global foreground Dice: [0.8996, 0.9456, 0.9388]\n",
      "2024-01-10 06:19:23.391295: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:19:24.308429: lr: 0.001259\n",
      "2024-01-10 06:19:24.337195: saving checkpoint...\n",
      "2024-01-10 06:19:31.314881: done, saving took 7.00 seconds\n",
      "2024-01-10 06:19:31.408382: This epoch took 514.840815 s\n",
      "\n",
      "2024-01-10 06:19:31.408512: \n",
      "epoch:  45\n",
      "2024-01-10 06:27:23.849661: train loss : -0.8767\n",
      "2024-01-10 06:27:57.494282: validation loss: -0.8442\n",
      "2024-01-10 06:27:57.495176: Average global foreground Dice: [0.8982, 0.9453, 0.9385]\n",
      "2024-01-10 06:27:57.495354: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:27:58.197026: lr: 0.00103\n",
      "2024-01-10 06:27:58.197261: This epoch took 506.788574 s\n",
      "\n",
      "2024-01-10 06:27:58.197391: \n",
      "epoch:  46\n",
      "2024-01-10 06:35:51.886532: train loss : -0.8779\n",
      "2024-01-10 06:36:25.709425: validation loss: -0.8451\n",
      "2024-01-10 06:36:25.710000: Average global foreground Dice: [0.9011, 0.9454, 0.9384]\n",
      "2024-01-10 06:36:25.710151: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:36:26.490883: lr: 0.000795\n",
      "2024-01-10 06:36:26.521965: saving checkpoint...\n",
      "2024-01-10 06:36:27.090811: done, saving took 0.60 seconds\n",
      "2024-01-10 06:36:27.092033: This epoch took 508.894525 s\n",
      "\n",
      "2024-01-10 06:36:27.092203: \n",
      "epoch:  47\n",
      "2024-01-10 06:44:21.221421: train loss : -0.8781\n",
      "2024-01-10 06:44:54.954064: validation loss: -0.8455\n",
      "2024-01-10 06:44:54.954533: Average global foreground Dice: [0.9001, 0.946, 0.9388]\n",
      "2024-01-10 06:44:54.954618: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:44:55.585981: lr: 0.000552\n",
      "2024-01-10 06:44:55.616422: saving checkpoint...\n",
      "2024-01-10 06:44:56.205574: done, saving took 0.62 seconds\n",
      "2024-01-10 06:44:56.206368: This epoch took 509.113991 s\n",
      "\n",
      "2024-01-10 06:44:56.206451: \n",
      "epoch:  48\n",
      "2024-01-10 06:52:50.518507: train loss : -0.8788\n",
      "2024-01-10 06:53:24.283994: validation loss: -0.8443\n",
      "2024-01-10 06:53:24.284594: Average global foreground Dice: [0.9003, 0.9455, 0.9383]\n",
      "2024-01-10 06:53:24.284714: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 06:53:25.027767: lr: 0.000296\n",
      "2024-01-10 06:53:25.058633: saving checkpoint...\n",
      "2024-01-10 06:53:25.656176: done, saving took 0.63 seconds\n",
      "2024-01-10 06:53:25.657242: This epoch took 509.450686 s\n",
      "\n",
      "2024-01-10 06:53:25.657348: \n",
      "epoch:  49\n",
      "2024-01-10 07:01:20.226830: train loss : -0.8785\n",
      "2024-01-10 07:01:53.929080: validation loss: -0.8460\n",
      "2024-01-10 07:01:53.929615: Average global foreground Dice: [0.8991, 0.9459, 0.9387]\n",
      "2024-01-10 07:01:53.929733: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:01:54.644418: lr: 0.0\n",
      "2024-01-10 07:01:54.644634: saving scheduled checkpoint file...\n",
      "2024-01-10 07:01:54.668534: saving checkpoint...\n",
      "2024-01-10 07:01:54.930412: done, saving took 0.29 seconds\n",
      "2024-01-10 07:01:54.931489: done\n",
      "2024-01-10 07:01:54.946730: saving checkpoint...\n",
      "2024-01-10 07:01:57.481709: done, saving took 2.55 seconds\n",
      "2024-01-10 07:01:57.487770: This epoch took 511.830338 s\n",
      "\n",
      "2024-01-10 07:01:57.533560: saving checkpoint...\n",
      "2024-01-10 07:02:01.432181: done, saving took 3.94 seconds\n",
      "IBSR_04 (2, 115, 137, 140)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "IBSR_07 (2, 109, 139, 144)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "IBSR_09 (2, 115, 142, 149)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "2024-01-10 07:02:27.817168: finished prediction\n",
      "2024-01-10 07:02:27.817489: evaluation of raw predictions\n",
      "2024-01-10 07:02:28.952180: determining postprocessing\n",
      "Foreground vs background\n",
      "before: 0.929951529111101\n",
      "after:  0.9299518677449989\n",
      "Removing all but the largest foreground region improved results!\n",
      "for_which_classes [1, 2, 3]\n",
      "min_valid_object_sizes None\n",
      "1\n",
      "before: 0.8975800112541773\n",
      "after:  0.8504713988151137\n",
      "2\n",
      "before: 0.9486599818899825\n",
      "after:  0.9486924070566177\n",
      "Removing all but the largest region for class 2 improved results!\n",
      "min_valid_object_sizes None\n",
      "3\n",
      "before: 0.9436156100908372\n",
      "after:  0.8924176101572692\n",
      "done\n",
      "for which classes:\n",
      "[[1, 2, 3], 2]\n",
      "min_object_sizes\n",
      "None\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train 2d nnUNetTrainerV2_Fast Task975_BrainSegmentation 1 --npz -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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_Fast.nnUNetTrainerV2_Fast'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  3\n",
      "modalities:  {0: 'T1'}\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': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2024-01-10 07:02:41.090295: Using splits from existing split file: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/splits_final.pkl\n",
      "2024-01-10 07:02:41.090843: The split file contains 5 splits.\n",
      "2024-01-10 07:02:41.090935: Desired fold for training: 2\n",
      "2024-01-10 07:02:41.091014: This split has 12 training and 3 validation cases.\n",
      "unpacking dataset\n",
      "done\n",
      "2024-01-10 07:02:42.051844: loading checkpoint /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_2/model_final_checkpoint.model train= True\n",
      "2024-01-10 07:02:42.553978: lr: 0.004384\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'instance_norm' is set to train=True. Exporting with train=True.\n",
      "  warnings.warn(\n",
      "2024-01-10 07:02:55.357198: Unable to plot network architecture:\n",
      "2024-01-10 07:02:55.357376: failed to execute ['dot', '-Tpdf', '-O', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_2/network_architecture'], make sure the Graphviz executables are on your systems' PATH\n",
      "2024-01-10 07:02:55.357503: \n",
      "printing the network instead:\n",
      "\n",
      "2024-01-10 07:02:55.357611: 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, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2024-01-10 07:02:55.361626: \n",
      "\n",
      "2024-01-10 07:02:55.364229: \n",
      "epoch:  30\n",
      "2024-01-10 07:12:01.146614: train loss : -0.8693\n",
      "2024-01-10 07:12:34.785784: validation loss: -0.8424\n",
      "2024-01-10 07:12:34.786428: Average global foreground Dice: [0.9065, 0.9464, 0.9329]\n",
      "2024-01-10 07:12:34.786683: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:12:35.481392: lr: 0.004186\n",
      "2024-01-10 07:12:35.537086: saving checkpoint...\n",
      "2024-01-10 07:12:36.505662: done, saving took 1.02 seconds\n",
      "2024-01-10 07:12:36.506524: This epoch took 581.141953 s\n",
      "\n",
      "2024-01-10 07:12:36.506608: \n",
      "epoch:  31\n",
      "2024-01-10 07:20:34.981994: train loss : -0.8692\n",
      "2024-01-10 07:21:08.769763: validation loss: -0.8451\n",
      "2024-01-10 07:21:08.770541: Average global foreground Dice: [0.906, 0.9463, 0.9333]\n",
      "2024-01-10 07:21:08.770676: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:21:09.374591: lr: 0.003987\n",
      "2024-01-10 07:21:09.374774: This epoch took 512.868016 s\n",
      "\n",
      "2024-01-10 07:21:09.374850: \n",
      "epoch:  32\n",
      "2024-01-10 07:29:07.891228: train loss : -0.8708\n",
      "2024-01-10 07:29:41.451358: validation loss: -0.8447\n",
      "2024-01-10 07:29:41.451881: Average global foreground Dice: [0.9036, 0.947, 0.9338]\n",
      "2024-01-10 07:29:41.451994: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:29:42.069704: lr: 0.003787\n",
      "2024-01-10 07:29:42.069884: This epoch took 512.694963 s\n",
      "\n",
      "2024-01-10 07:29:42.069966: \n",
      "epoch:  33\n",
      "2024-01-10 07:37:39.941523: train loss : -0.8712\n",
      "2024-01-10 07:38:13.713071: validation loss: -0.8445\n",
      "2024-01-10 07:38:13.713620: Average global foreground Dice: [0.9078, 0.9468, 0.9331]\n",
      "2024-01-10 07:38:13.713763: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:38:14.446642: lr: 0.003586\n",
      "2024-01-10 07:38:14.484430: saving checkpoint...\n",
      "2024-01-10 07:38:15.321537: done, saving took 0.87 seconds\n",
      "2024-01-10 07:38:15.322343: This epoch took 513.252267 s\n",
      "\n",
      "2024-01-10 07:38:15.322415: \n",
      "epoch:  34\n",
      "2024-01-10 07:46:13.210998: train loss : -0.8728\n",
      "2024-01-10 07:46:46.949013: validation loss: -0.8473\n",
      "2024-01-10 07:46:46.949866: Average global foreground Dice: [0.9075, 0.947, 0.9338]\n",
      "2024-01-10 07:46:46.950020: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:46:47.716351: lr: 0.003384\n",
      "2024-01-10 07:46:47.757226: saving checkpoint...\n",
      "2024-01-10 07:46:48.357588: done, saving took 0.64 seconds\n",
      "2024-01-10 07:46:48.358375: This epoch took 513.035822 s\n",
      "\n",
      "2024-01-10 07:46:48.358448: \n",
      "epoch:  35\n",
      "2024-01-10 07:54:47.119746: train loss : -0.8721\n",
      "2024-01-10 07:55:20.823525: validation loss: -0.8449\n",
      "2024-01-10 07:55:20.824166: Average global foreground Dice: [0.9063, 0.9464, 0.933]\n",
      "2024-01-10 07:55:20.824312: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 07:55:21.553751: lr: 0.00318\n",
      "2024-01-10 07:55:21.553990: This epoch took 513.195433 s\n",
      "\n",
      "2024-01-10 07:55:21.554144: \n",
      "epoch:  36\n",
      "2024-01-10 08:03:19.908138: train loss : -0.8737\n",
      "2024-01-10 08:03:53.589290: validation loss: -0.8465\n",
      "2024-01-10 08:03:53.589816: Average global foreground Dice: [0.9079, 0.9467, 0.9349]\n",
      "2024-01-10 08:03:53.589928: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:03:54.481026: lr: 0.002975\n",
      "2024-01-10 08:03:54.496534: saving checkpoint...\n",
      "2024-01-10 08:03:55.070806: done, saving took 0.59 seconds\n",
      "2024-01-10 08:03:55.072116: This epoch took 513.517841 s\n",
      "\n",
      "2024-01-10 08:03:55.072177: \n",
      "epoch:  37\n",
      "2024-01-10 08:11:52.867068: train loss : -0.8739\n",
      "2024-01-10 08:12:26.638483: validation loss: -0.8457\n",
      "2024-01-10 08:12:26.639001: Average global foreground Dice: [0.9062, 0.9475, 0.9334]\n",
      "2024-01-10 08:12:26.639108: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:12:27.294134: lr: 0.002768\n",
      "2024-01-10 08:12:27.310854: saving checkpoint...\n",
      "2024-01-10 08:12:27.877167: done, saving took 0.58 seconds\n",
      "2024-01-10 08:12:27.877755: This epoch took 512.805497 s\n",
      "\n",
      "2024-01-10 08:12:27.877808: \n",
      "epoch:  38\n",
      "2024-01-10 08:20:26.858947: train loss : -0.8747\n",
      "2024-01-10 08:21:00.523733: validation loss: -0.8469\n",
      "2024-01-10 08:21:00.524308: Average global foreground Dice: [0.9063, 0.9481, 0.9344]\n",
      "2024-01-10 08:21:00.524421: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:21:01.246140: lr: 0.00256\n",
      "2024-01-10 08:21:01.260203: saving checkpoint...\n",
      "2024-01-10 08:21:01.850747: done, saving took 0.60 seconds\n",
      "2024-01-10 08:21:01.851521: This epoch took 513.973645 s\n",
      "\n",
      "2024-01-10 08:21:01.851576: \n",
      "epoch:  39\n",
      "2024-01-10 08:29:00.590233: train loss : -0.8747\n",
      "2024-01-10 08:29:34.314614: validation loss: -0.8475\n",
      "2024-01-10 08:29:34.315113: Average global foreground Dice: [0.9079, 0.9474, 0.9345]\n",
      "2024-01-10 08:29:34.315215: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:29:34.978400: lr: 0.002349\n",
      "2024-01-10 08:29:34.995109: saving checkpoint...\n",
      "2024-01-10 08:29:35.556240: done, saving took 0.58 seconds\n",
      "2024-01-10 08:29:35.557166: This epoch took 513.705541 s\n",
      "\n",
      "2024-01-10 08:29:35.557261: \n",
      "epoch:  40\n",
      "2024-01-10 08:37:33.219622: train loss : -0.8757\n",
      "2024-01-10 08:38:06.799533: validation loss: -0.8469\n",
      "2024-01-10 08:38:06.799990: Average global foreground Dice: [0.908, 0.9477, 0.9338]\n",
      "2024-01-10 08:38:06.800087: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:38:07.449479: lr: 0.002137\n",
      "2024-01-10 08:38:07.464035: saving checkpoint...\n",
      "2024-01-10 08:38:08.042267: done, saving took 0.59 seconds\n",
      "2024-01-10 08:38:08.043019: This epoch took 512.485624 s\n",
      "\n",
      "2024-01-10 08:38:08.043088: \n",
      "epoch:  41\n",
      "2024-01-10 08:46:08.869469: train loss : -0.8760\n",
      "2024-01-10 08:46:42.577075: validation loss: -0.8491\n",
      "2024-01-10 08:46:42.577594: Average global foreground Dice: [0.9101, 0.948, 0.9345]\n",
      "2024-01-10 08:46:42.577689: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:46:43.177280: lr: 0.001922\n",
      "2024-01-10 08:46:43.190304: saving checkpoint...\n",
      "2024-01-10 08:46:43.752643: done, saving took 0.58 seconds\n",
      "2024-01-10 08:46:43.753577: This epoch took 515.710393 s\n",
      "\n",
      "2024-01-10 08:46:43.753642: \n",
      "epoch:  42\n",
      "2024-01-10 08:54:42.659217: train loss : -0.8764\n",
      "2024-01-10 08:55:16.254637: validation loss: -0.8478\n",
      "2024-01-10 08:55:16.255355: Average global foreground Dice: [0.9083, 0.948, 0.9341]\n",
      "2024-01-10 08:55:16.255486: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 08:55:16.992359: lr: 0.001704\n",
      "2024-01-10 08:55:17.008087: saving checkpoint...\n",
      "2024-01-10 08:55:19.246606: done, saving took 2.25 seconds\n",
      "2024-01-10 08:55:19.273802: This epoch took 515.520057 s\n",
      "\n",
      "2024-01-10 08:55:19.274035: \n",
      "epoch:  43\n",
      "2024-01-10 09:03:17.682464: train loss : -0.8770\n",
      "2024-01-10 09:03:51.592280: validation loss: -0.8451\n",
      "2024-01-10 09:03:51.592785: Average global foreground Dice: [0.9055, 0.9472, 0.9338]\n",
      "2024-01-10 09:03:51.592879: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:03:52.165930: lr: 0.001483\n",
      "2024-01-10 09:03:52.166128: This epoch took 512.891963 s\n",
      "\n",
      "2024-01-10 09:03:52.166223: \n",
      "epoch:  44\n",
      "2024-01-10 09:11:51.774945: train loss : -0.8777\n",
      "2024-01-10 09:12:25.563686: validation loss: -0.8466\n",
      "2024-01-10 09:12:25.564377: Average global foreground Dice: [0.9084, 0.9478, 0.9344]\n",
      "2024-01-10 09:12:25.564533: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:12:26.259880: lr: 0.001259\n",
      "2024-01-10 09:12:26.281239: saving checkpoint...\n",
      "2024-01-10 09:12:26.852928: done, saving took 0.59 seconds\n",
      "2024-01-10 09:12:26.853963: This epoch took 514.687594 s\n",
      "\n",
      "2024-01-10 09:12:26.854086: \n",
      "epoch:  45\n",
      "2024-01-10 09:20:26.391482: train loss : -0.8772\n",
      "2024-01-10 09:21:00.169569: validation loss: -0.8482\n",
      "2024-01-10 09:21:00.170180: Average global foreground Dice: [0.9079, 0.9486, 0.9342]\n",
      "2024-01-10 09:21:00.170322: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:21:01.017807: lr: 0.00103\n",
      "2024-01-10 09:21:01.037640: saving checkpoint...\n",
      "2024-01-10 09:21:01.601694: done, saving took 0.58 seconds\n",
      "2024-01-10 09:21:01.602494: This epoch took 514.748324 s\n",
      "\n",
      "2024-01-10 09:21:01.602570: \n",
      "epoch:  46\n",
      "2024-01-10 09:29:01.255939: train loss : -0.8782\n",
      "2024-01-10 09:29:34.996950: validation loss: -0.8462\n",
      "2024-01-10 09:29:34.997490: Average global foreground Dice: [0.9063, 0.947, 0.9337]\n",
      "2024-01-10 09:29:34.997596: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:29:35.628768: lr: 0.000795\n",
      "2024-01-10 09:29:35.628952: This epoch took 514.026285 s\n",
      "\n",
      "2024-01-10 09:29:35.629036: \n",
      "epoch:  47\n",
      "2024-01-10 09:37:35.112351: train loss : -0.8782\n",
      "2024-01-10 09:38:08.840236: validation loss: -0.8501\n",
      "2024-01-10 09:38:08.840861: Average global foreground Dice: [0.9113, 0.9484, 0.9351]\n",
      "2024-01-10 09:38:08.840974: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:38:09.494240: lr: 0.000552\n",
      "2024-01-10 09:38:09.513392: saving checkpoint...\n",
      "2024-01-10 09:38:10.053203: done, saving took 0.56 seconds\n",
      "2024-01-10 09:38:10.053959: This epoch took 514.424841 s\n",
      "\n",
      "2024-01-10 09:38:10.054064: \n",
      "epoch:  48\n",
      "2024-01-10 09:46:09.372287: train loss : -0.8780\n",
      "2024-01-10 09:46:43.173602: validation loss: -0.8460\n",
      "2024-01-10 09:46:43.174175: Average global foreground Dice: [0.9079, 0.9475, 0.9345]\n",
      "2024-01-10 09:46:43.174301: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:46:43.888373: lr: 0.000296\n",
      "2024-01-10 09:46:43.905392: saving checkpoint...\n",
      "2024-01-10 09:46:44.507540: done, saving took 0.62 seconds\n",
      "2024-01-10 09:46:44.508476: This epoch took 514.454281 s\n",
      "\n",
      "2024-01-10 09:46:44.508551: \n",
      "epoch:  49\n",
      "2024-01-10 09:54:44.734136: train loss : -0.8789\n",
      "2024-01-10 09:55:18.692191: validation loss: -0.8473\n",
      "2024-01-10 09:55:18.692747: Average global foreground Dice: [0.9093, 0.9482, 0.9337]\n",
      "2024-01-10 09:55:18.692848: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 09:55:19.282732: lr: 0.0\n",
      "2024-01-10 09:55:19.282891: saving scheduled checkpoint file...\n",
      "2024-01-10 09:55:19.298613: saving checkpoint...\n",
      "2024-01-10 09:55:19.499649: done, saving took 0.22 seconds\n",
      "2024-01-10 09:55:19.500446: done\n",
      "2024-01-10 09:55:19.520403: saving checkpoint...\n",
      "2024-01-10 09:55:20.046969: done, saving took 0.55 seconds\n",
      "2024-01-10 09:55:20.048473: This epoch took 515.539849 s\n",
      "\n",
      "2024-01-10 09:55:20.067455: saving checkpoint...\n",
      "2024-01-10 09:55:20.585626: done, saving took 0.54 seconds\n",
      "IBSR_11 (2, 109, 139, 154)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "IBSR_13 (2, 115, 136, 149)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "IBSR_15 (2, 120, 147, 153)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "2024-01-10 09:55:48.854787: finished prediction\n",
      "2024-01-10 09:55:48.855277: evaluation of raw predictions\n",
      "2024-01-10 09:55:50.072049: determining postprocessing\n",
      "Foreground vs background\n",
      "before: 0.9334241925831116\n",
      "after:  0.9334221767126286\n",
      "1\n",
      "before: 0.9099180077927803\n",
      "after:  0.8315666003174537\n",
      "2\n",
      "before: 0.952176823629808\n",
      "after:  0.9521751315313557\n",
      "3\n",
      "before: 0.9381777463267463\n",
      "after:  0.888751985943444\n",
      "done\n",
      "for which classes:\n",
      "[]\n",
      "min_object_sizes\n",
      "None\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train 2d nnUNetTrainerV2_Fast Task975_BrainSegmentation 2 --npz -c "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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_Fast.nnUNetTrainerV2_Fast'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  3\n",
      "modalities:  {0: 'T1'}\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': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2024-01-10 09:56:02.895829: Using splits from existing split file: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/splits_final.pkl\n",
      "2024-01-10 09:56:02.896519: The split file contains 5 splits.\n",
      "2024-01-10 09:56:02.896612: Desired fold for training: 4\n",
      "2024-01-10 09:56:02.896676: This split has 12 training and 3 validation cases.\n",
      "unpacking dataset\n",
      "done\n",
      "2024-01-10 09:56:03.953390: loading checkpoint /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_4/model_final_checkpoint.model train= True\n",
      "2024-01-10 09:56:04.465642: lr: 0.004384\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'instance_norm' is set to train=True. Exporting with train=True.\n",
      "  warnings.warn(\n",
      "2024-01-10 09:56:16.938197: Unable to plot network architecture:\n",
      "2024-01-10 09:56:16.939148: failed to execute ['dot', '-Tpdf', '-O', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_4/network_architecture'], make sure the Graphviz executables are on your systems' PATH\n",
      "2024-01-10 09:56:16.940191: \n",
      "printing the network instead:\n",
      "\n",
      "2024-01-10 09:56:16.941257: 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, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2024-01-10 09:56:16.968380: \n",
      "\n",
      "2024-01-10 09:56:16.976038: \n",
      "epoch:  30\n",
      "2024-01-10 10:05:25.784331: train loss : -0.8706\n",
      "2024-01-10 10:05:59.595636: validation loss: -0.8336\n",
      "2024-01-10 10:05:59.596219: Average global foreground Dice: [0.8655, 0.9459, 0.9304]\n",
      "2024-01-10 10:05:59.596365: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:06:00.380170: lr: 0.004186\n",
      "2024-01-10 10:06:00.441298: saving checkpoint...\n",
      "2024-01-10 10:06:01.077798: done, saving took 0.70 seconds\n",
      "2024-01-10 10:06:01.078535: This epoch took 584.101455 s\n",
      "\n",
      "2024-01-10 10:06:01.078671: \n",
      "epoch:  31\n",
      "2024-01-10 10:14:00.629033: train loss : -0.8704\n",
      "2024-01-10 10:14:34.305002: validation loss: -0.8385\n",
      "2024-01-10 10:14:34.305601: Average global foreground Dice: [0.8737, 0.9466, 0.9304]\n",
      "2024-01-10 10:14:34.305742: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:14:35.025536: lr: 0.003987\n",
      "2024-01-10 10:14:35.043720: saving checkpoint...\n",
      "2024-01-10 10:14:35.653323: done, saving took 0.63 seconds\n",
      "2024-01-10 10:14:35.654519: This epoch took 514.575788 s\n",
      "\n",
      "2024-01-10 10:14:35.654649: \n",
      "epoch:  32\n",
      "2024-01-10 10:22:35.172976: train loss : -0.8724\n",
      "2024-01-10 10:23:08.878899: validation loss: -0.8393\n",
      "2024-01-10 10:23:08.879412: Average global foreground Dice: [0.874, 0.9468, 0.9322]\n",
      "2024-01-10 10:23:08.879517: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:23:09.542180: lr: 0.003787\n",
      "2024-01-10 10:23:09.575097: saving checkpoint...\n",
      "2024-01-10 10:23:10.160849: done, saving took 0.62 seconds\n",
      "2024-01-10 10:23:10.161876: This epoch took 514.507135 s\n",
      "\n",
      "2024-01-10 10:23:10.162036: \n",
      "epoch:  33\n",
      "2024-01-10 10:31:10.390525: train loss : -0.8723\n",
      "2024-01-10 10:31:44.133213: validation loss: -0.8347\n",
      "2024-01-10 10:31:44.133988: Average global foreground Dice: [0.8696, 0.9461, 0.9295]\n",
      "2024-01-10 10:31:44.134138: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:31:44.742280: lr: 0.003586\n",
      "2024-01-10 10:31:44.776044: saving checkpoint...\n",
      "2024-01-10 10:31:47.928808: done, saving took 3.19 seconds\n",
      "2024-01-10 10:31:47.929922: This epoch took 517.767774 s\n",
      "\n",
      "2024-01-10 10:31:47.929989: \n",
      "epoch:  34\n",
      "2024-01-10 10:39:48.586395: train loss : -0.8740\n",
      "2024-01-10 10:40:22.444985: validation loss: -0.8380\n",
      "2024-01-10 10:40:22.445473: Average global foreground Dice: [0.8687, 0.9471, 0.9333]\n",
      "2024-01-10 10:40:22.445584: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:40:23.102039: lr: 0.003384\n",
      "2024-01-10 10:40:23.136884: saving checkpoint...\n",
      "2024-01-10 10:40:28.676149: done, saving took 5.57 seconds\n",
      "2024-01-10 10:40:28.676987: This epoch took 520.746895 s\n",
      "\n",
      "2024-01-10 10:40:28.677044: \n",
      "epoch:  35\n",
      "2024-01-10 10:48:28.741244: train loss : -0.8742\n",
      "2024-01-10 10:49:02.458384: validation loss: -0.8385\n",
      "2024-01-10 10:49:02.459000: Average global foreground Dice: [0.8654, 0.9476, 0.9319]\n",
      "2024-01-10 10:49:02.459122: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:49:03.216882: lr: 0.00318\n",
      "2024-01-10 10:49:03.254917: saving checkpoint...\n",
      "2024-01-10 10:49:03.845474: done, saving took 0.63 seconds\n",
      "2024-01-10 10:49:03.846686: This epoch took 515.169543 s\n",
      "\n",
      "2024-01-10 10:49:03.846774: \n",
      "epoch:  36\n",
      "2024-01-10 10:57:04.048306: train loss : -0.8752\n",
      "2024-01-10 10:57:37.838534: validation loss: -0.8351\n",
      "2024-01-10 10:57:37.839032: Average global foreground Dice: [0.8688, 0.9466, 0.9302]\n",
      "2024-01-10 10:57:37.839148: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 10:57:38.593935: lr: 0.002975\n",
      "2024-01-10 10:57:38.627612: saving checkpoint...\n",
      "2024-01-10 10:57:39.166145: done, saving took 0.57 seconds\n",
      "2024-01-10 10:57:39.167572: This epoch took 515.320703 s\n",
      "\n",
      "2024-01-10 10:57:39.167718: \n",
      "epoch:  37\n",
      "2024-01-10 11:05:39.847318: train loss : -0.8758\n",
      "2024-01-10 11:06:13.583875: validation loss: -0.8385\n",
      "2024-01-10 11:06:13.584510: Average global foreground Dice: [0.8697, 0.9475, 0.9328]\n",
      "2024-01-10 11:06:13.584625: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:06:14.229903: lr: 0.002768\n",
      "2024-01-10 11:06:14.247039: saving checkpoint...\n",
      "2024-01-10 11:06:14.817917: done, saving took 0.59 seconds\n",
      "2024-01-10 11:06:14.818586: This epoch took 515.650755 s\n",
      "\n",
      "2024-01-10 11:06:14.818641: \n",
      "epoch:  38\n",
      "2024-01-10 11:14:15.353947: train loss : -0.8765\n",
      "2024-01-10 11:14:49.190937: validation loss: -0.8351\n",
      "2024-01-10 11:14:49.191470: Average global foreground Dice: [0.867, 0.947, 0.9311]\n",
      "2024-01-10 11:14:49.191584: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:14:49.938345: lr: 0.00256\n",
      "2024-01-10 11:14:49.938684: This epoch took 515.119959 s\n",
      "\n",
      "2024-01-10 11:14:49.938785: \n",
      "epoch:  39\n",
      "2024-01-10 11:22:49.697492: train loss : -0.8770\n",
      "2024-01-10 11:23:23.453675: validation loss: -0.8397\n",
      "2024-01-10 11:23:23.454485: Average global foreground Dice: [0.8717, 0.9479, 0.9319]\n",
      "2024-01-10 11:23:23.454645: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:23:24.133956: lr: 0.002349\n",
      "2024-01-10 11:23:24.171014: saving checkpoint...\n",
      "2024-01-10 11:23:24.768738: done, saving took 0.63 seconds\n",
      "2024-01-10 11:23:24.769733: This epoch took 514.830853 s\n",
      "\n",
      "2024-01-10 11:23:24.769821: \n",
      "epoch:  40\n",
      "2024-01-10 11:31:24.644981: train loss : -0.8782\n",
      "2024-01-10 11:31:58.341693: validation loss: -0.8383\n",
      "2024-01-10 11:31:58.342395: Average global foreground Dice: [0.8712, 0.9471, 0.9316]\n",
      "2024-01-10 11:31:58.342533: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:31:58.985884: lr: 0.002137\n",
      "2024-01-10 11:31:59.005829: saving checkpoint...\n",
      "2024-01-10 11:31:59.541280: done, saving took 0.56 seconds\n",
      "2024-01-10 11:31:59.542248: This epoch took 514.772312 s\n",
      "\n",
      "2024-01-10 11:31:59.542335: \n",
      "epoch:  41\n",
      "2024-01-10 11:40:02.063478: train loss : -0.8784\n",
      "2024-01-10 11:40:36.148541: validation loss: -0.8407\n",
      "2024-01-10 11:40:36.149109: Average global foreground Dice: [0.8738, 0.9477, 0.9324]\n",
      "2024-01-10 11:40:36.149228: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:40:36.790576: lr: 0.001922\n",
      "2024-01-10 11:40:36.810712: saving checkpoint...\n",
      "2024-01-10 11:40:39.879431: done, saving took 3.09 seconds\n",
      "2024-01-10 11:40:39.881311: This epoch took 520.338843 s\n",
      "\n",
      "2024-01-10 11:40:39.881412: \n",
      "epoch:  42\n",
      "2024-01-10 11:48:43.008013: train loss : -0.8789\n",
      "2024-01-10 11:49:17.046593: validation loss: -0.8395\n",
      "2024-01-10 11:49:17.047161: Average global foreground Dice: [0.8702, 0.9482, 0.9329]\n",
      "2024-01-10 11:49:17.047273: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:49:17.724497: lr: 0.001704\n",
      "2024-01-10 11:49:17.750670: saving checkpoint...\n",
      "2024-01-10 11:49:19.519477: done, saving took 1.79 seconds\n",
      "2024-01-10 11:49:19.521011: This epoch took 519.639507 s\n",
      "\n",
      "2024-01-10 11:49:19.521183: \n",
      "epoch:  43\n",
      "2024-01-10 11:57:23.248407: train loss : -0.8799\n",
      "2024-01-10 11:57:57.295478: validation loss: -0.8368\n",
      "2024-01-10 11:57:57.296019: Average global foreground Dice: [0.8689, 0.9471, 0.9318]\n",
      "2024-01-10 11:57:57.296162: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 11:57:58.002499: lr: 0.001483\n",
      "2024-01-10 11:57:58.023149: saving checkpoint...\n",
      "2024-01-10 11:57:58.673824: done, saving took 0.67 seconds\n",
      "2024-01-10 11:57:58.700264: This epoch took 519.178982 s\n",
      "\n",
      "2024-01-10 11:57:58.700536: \n",
      "epoch:  44\n",
      "2024-01-10 12:06:00.956141: train loss : -0.8797\n",
      "2024-01-10 12:06:34.950706: validation loss: -0.8386\n",
      "2024-01-10 12:06:34.951250: Average global foreground Dice: [0.8742, 0.9469, 0.9322]\n",
      "2024-01-10 12:06:34.951363: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 12:06:35.632256: lr: 0.001259\n",
      "2024-01-10 12:06:35.654051: saving checkpoint...\n",
      "2024-01-10 12:06:36.202411: done, saving took 0.57 seconds\n",
      "2024-01-10 12:06:36.229663: This epoch took 517.528985 s\n",
      "\n",
      "2024-01-10 12:06:36.229810: \n",
      "epoch:  45\n",
      "2024-01-10 12:14:38.023625: train loss : -0.8807\n",
      "2024-01-10 12:15:11.950880: validation loss: -0.8371\n",
      "2024-01-10 12:15:11.951393: Average global foreground Dice: [0.8719, 0.9471, 0.932]\n",
      "2024-01-10 12:15:11.951531: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 12:15:12.839844: lr: 0.00103\n",
      "2024-01-10 12:15:12.860512: saving checkpoint...\n",
      "2024-01-10 12:15:13.458092: done, saving took 0.62 seconds\n",
      "2024-01-10 12:15:13.459282: This epoch took 517.229399 s\n",
      "\n",
      "2024-01-10 12:15:13.459358: \n",
      "epoch:  46\n",
      "2024-01-10 12:23:15.710753: train loss : -0.8807\n",
      "2024-01-10 12:23:49.835001: validation loss: -0.8392\n",
      "2024-01-10 12:23:49.835595: Average global foreground Dice: [0.8716, 0.948, 0.9332]\n",
      "2024-01-10 12:23:49.835734: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 12:23:50.583677: lr: 0.000795\n",
      "2024-01-10 12:23:50.599720: saving checkpoint...\n",
      "2024-01-10 12:23:51.174746: done, saving took 0.59 seconds\n",
      "2024-01-10 12:23:51.175689: This epoch took 517.716214 s\n",
      "\n",
      "2024-01-10 12:23:51.175754: \n",
      "epoch:  47\n",
      "2024-01-10 12:31:53.630028: train loss : -0.8811\n",
      "2024-01-10 12:32:27.570939: validation loss: -0.8387\n",
      "2024-01-10 12:32:27.571447: Average global foreground Dice: [0.8716, 0.9473, 0.9319]\n",
      "2024-01-10 12:32:27.571556: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 12:32:28.246285: lr: 0.000552\n",
      "2024-01-10 12:32:28.263555: saving checkpoint...\n",
      "2024-01-10 12:32:28.828238: done, saving took 0.58 seconds\n",
      "2024-01-10 12:32:28.829475: This epoch took 517.653662 s\n",
      "\n",
      "2024-01-10 12:32:28.829559: \n",
      "epoch:  48\n",
      "2024-01-10 12:40:31.727610: train loss : -0.8811\n",
      "2024-01-10 12:41:05.852170: validation loss: -0.8375\n",
      "2024-01-10 12:41:05.852752: Average global foreground Dice: [0.8675, 0.948, 0.932]\n",
      "2024-01-10 12:41:05.852941: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 12:41:06.507404: lr: 0.000296\n",
      "2024-01-10 12:41:06.507639: This epoch took 517.677985 s\n",
      "\n",
      "2024-01-10 12:41:06.507737: \n",
      "epoch:  49\n",
      "2024-01-10 12:49:07.870569: train loss : -0.8813\n",
      "2024-01-10 12:49:41.208973: validation loss: -0.8386\n",
      "2024-01-10 12:49:41.209459: Average global foreground Dice: [0.8707, 0.9478, 0.933]\n",
      "2024-01-10 12:49:41.209571: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 12:49:41.775435: lr: 0.0\n",
      "2024-01-10 12:49:41.775618: saving scheduled checkpoint file...\n",
      "2024-01-10 12:49:41.788059: saving checkpoint...\n",
      "2024-01-10 12:49:41.990693: done, saving took 0.21 seconds\n",
      "2024-01-10 12:49:41.991775: done\n",
      "2024-01-10 12:49:42.004226: saving checkpoint...\n",
      "2024-01-10 12:49:42.543182: done, saving took 0.55 seconds\n",
      "2024-01-10 12:49:42.543932: This epoch took 516.036095 s\n",
      "\n",
      "2024-01-10 12:49:42.555640: saving checkpoint...\n",
      "2024-01-10 12:49:43.071007: done, saving took 0.53 seconds\n",
      "IBSR_02 (2, 111, 131, 143)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "IBSR_03 (2, 116, 134, 151)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "IBSR_06 (2, 113, 137, 141)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "2024-01-10 12:50:08.341230: finished prediction\n",
      "2024-01-10 12:50:08.341596: evaluation of raw predictions\n",
      "2024-01-10 12:50:09.360493: determining postprocessing\n",
      "Foreground vs background\n",
      "before: 0.9199295151039298\n",
      "after:  0.91992970983059\n",
      "Removing all but the largest foreground region improved results!\n",
      "for_which_classes [1, 2, 3]\n",
      "min_valid_object_sizes None\n",
      "1\n",
      "before: 0.8742967804172989\n",
      "after:  0.7665903200034361\n",
      "2\n",
      "before: 0.9508445344957225\n",
      "after:  0.9508797109424014\n",
      "Removing all but the largest region for class 2 improved results!\n",
      "min_valid_object_sizes None\n",
      "3\n",
      "before: 0.9346478145787483\n",
      "after:  0.8979885629230999\n",
      "done\n",
      "for which classes:\n",
      "[[1, 2, 3], 2]\n",
      "min_object_sizes\n",
      "None\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train 2d nnUNetTrainerV2_Fast Task975_BrainSegmentation 4 --npz -c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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_Fast.nnUNetTrainerV2_Fast'>\n",
      "For that I will be using the following configuration:\n",
      "num_classes:  3\n",
      "modalities:  {0: 'T1'}\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': 69, 'num_pool_per_axis': [5, 5], 'patch_size': array([160, 160]), 'median_patient_size_in_voxels': array([115, 139, 147]), 'current_spacing': array([1.5   , 0.9375, 0.9375]), 'original_spacing': array([1.5   , 0.9375, 0.9375]), '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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/nnUNetData_plans_v2.1_2D\n",
      "###############################################\n",
      "loading dataset\n",
      "loading all case properties\n",
      "2024-01-10 12:50:22.524481: Using splits from existing split file: /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_preprocessed/Task975_BrainSegmentation/splits_final.pkl\n",
      "2024-01-10 12:50:22.524835: The split file contains 5 splits.\n",
      "2024-01-10 12:50:22.524916: Desired fold for training: 5\n",
      "2024-01-10 12:50:22.524989: INFO: You requested fold 5 for training but splits contain only 5 folds. I am now creating a random (but seeded) 80:20 split!\n",
      "2024-01-10 12:50:22.525759: This random 80:20 split has 12 training and 3 validation cases.\n",
      "unpacking dataset\n",
      "done\n",
      "2024-01-10 12:50:23.518598: loading checkpoint /home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_5/model_final_checkpoint.model train= True\n",
      "2024-01-10 12:50:24.053283: lr: 0.004384\n",
      "using pin_memory on device 0\n",
      "using pin_memory on device 0\n",
      "/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/projectMisaMira/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'instance_norm' is set to train=True. Exporting with train=True.\n",
      "  warnings.warn(\n",
      "2024-01-10 12:50:36.320684: Unable to plot network architecture:\n",
      "2024-01-10 12:50:36.321597: failed to execute ['dot', '-Tpdf', '-O', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_5/network_architecture'], make sure the Graphviz executables are on your systems' PATH\n",
      "2024-01-10 12:50:36.322425: \n",
      "printing the network instead:\n",
      "\n",
      "2024-01-10 12:50:36.323200: 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, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (3): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (4): Conv2d(32, 4, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  )\n",
      ")\n",
      "2024-01-10 12:50:36.335819: \n",
      "\n",
      "2024-01-10 12:50:36.339733: \n",
      "epoch:  30\n",
      "2024-01-10 12:59:39.358258: train loss : -0.8701\n",
      "2024-01-10 13:00:13.357246: validation loss: -0.8393\n",
      "2024-01-10 13:00:13.357806: Average global foreground Dice: [0.8909, 0.9461, 0.9317]\n",
      "2024-01-10 13:00:13.357962: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:00:13.976527: lr: 0.004186\n",
      "2024-01-10 13:00:14.028457: saving checkpoint...\n",
      "2024-01-10 13:00:14.667934: done, saving took 0.69 seconds\n",
      "2024-01-10 13:00:14.668929: This epoch took 578.328880 s\n",
      "\n",
      "2024-01-10 13:00:14.669020: \n",
      "epoch:  31\n",
      "2024-01-10 13:08:12.210872: train loss : -0.8703\n",
      "2024-01-10 13:08:46.018298: validation loss: -0.8349\n",
      "2024-01-10 13:08:46.018843: Average global foreground Dice: [0.8867, 0.9451, 0.9282]\n",
      "2024-01-10 13:08:46.019104: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:08:46.780312: lr: 0.003987\n",
      "2024-01-10 13:08:46.780519: This epoch took 512.111403 s\n",
      "\n",
      "2024-01-10 13:08:46.780613: \n",
      "epoch:  32\n",
      "2024-01-10 13:16:44.878268: train loss : -0.8719\n",
      "2024-01-10 13:17:18.841255: validation loss: -0.8363\n",
      "2024-01-10 13:17:18.841779: Average global foreground Dice: [0.8865, 0.9455, 0.9304]\n",
      "2024-01-10 13:17:18.841927: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:17:19.527890: lr: 0.003787\n",
      "2024-01-10 13:17:19.528079: This epoch took 512.747375 s\n",
      "\n",
      "2024-01-10 13:17:19.528185: \n",
      "epoch:  33\n",
      "2024-01-10 13:25:16.459944: train loss : -0.8726\n",
      "2024-01-10 13:25:50.361758: validation loss: -0.8374\n",
      "2024-01-10 13:25:50.362376: Average global foreground Dice: [0.8906, 0.9456, 0.9307]\n",
      "2024-01-10 13:25:50.362523: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:25:51.121708: lr: 0.003586\n",
      "2024-01-10 13:25:51.121947: This epoch took 511.593669 s\n",
      "\n",
      "2024-01-10 13:25:51.122115: \n",
      "epoch:  34\n",
      "2024-01-10 13:33:48.730563: train loss : -0.8733\n",
      "2024-01-10 13:34:22.582806: validation loss: -0.8397\n",
      "2024-01-10 13:34:22.583331: Average global foreground Dice: [0.8874, 0.9464, 0.932]\n",
      "2024-01-10 13:34:22.583464: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:34:23.306557: lr: 0.003384\n",
      "2024-01-10 13:34:23.306782: This epoch took 512.184564 s\n",
      "\n",
      "2024-01-10 13:34:23.306879: \n",
      "epoch:  35\n",
      "2024-01-10 13:42:21.697022: train loss : -0.8732\n",
      "2024-01-10 13:42:55.688381: validation loss: -0.8393\n",
      "2024-01-10 13:42:55.688923: Average global foreground Dice: [0.8902, 0.9458, 0.9308]\n",
      "2024-01-10 13:42:55.689036: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:42:56.442658: lr: 0.00318\n",
      "2024-01-10 13:42:56.442862: This epoch took 513.135892 s\n",
      "\n",
      "2024-01-10 13:42:56.442961: \n",
      "epoch:  36\n",
      "2024-01-10 13:50:54.324734: train loss : -0.8740\n",
      "2024-01-10 13:51:28.320004: validation loss: -0.8374\n",
      "2024-01-10 13:51:28.320504: Average global foreground Dice: [0.8894, 0.945, 0.9284]\n",
      "2024-01-10 13:51:28.320612: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 13:51:29.221312: lr: 0.002975\n",
      "2024-01-10 13:51:29.221526: This epoch took 512.778425 s\n",
      "\n",
      "2024-01-10 13:51:29.221629: \n",
      "epoch:  37\n",
      "2024-01-10 13:59:27.085470: train loss : -0.8751\n",
      "2024-01-10 14:00:00.991412: validation loss: -0.8367\n",
      "2024-01-10 14:00:00.992004: Average global foreground Dice: [0.8875, 0.9456, 0.9307]\n",
      "2024-01-10 14:00:00.992134: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:00:01.742537: lr: 0.002768\n",
      "2024-01-10 14:00:01.742760: This epoch took 512.521031 s\n",
      "\n",
      "2024-01-10 14:00:01.742862: \n",
      "epoch:  38\n",
      "2024-01-10 14:07:59.873781: train loss : -0.8760\n",
      "2024-01-10 14:08:33.787523: validation loss: -0.8380\n",
      "2024-01-10 14:08:33.788084: Average global foreground Dice: [0.8931, 0.945, 0.9297]\n",
      "2024-01-10 14:08:33.788235: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:08:34.546865: lr: 0.00256\n",
      "2024-01-10 14:08:34.547126: This epoch took 512.804166 s\n",
      "\n",
      "2024-01-10 14:08:34.547265: \n",
      "epoch:  39\n",
      "2024-01-10 14:16:32.417524: train loss : -0.8758\n",
      "2024-01-10 14:17:06.248107: validation loss: -0.8362\n",
      "2024-01-10 14:17:06.248744: Average global foreground Dice: [0.8826, 0.9468, 0.9316]\n",
      "2024-01-10 14:17:06.248896: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:17:07.012912: lr: 0.002349\n",
      "2024-01-10 14:17:07.013170: This epoch took 512.465765 s\n",
      "\n",
      "2024-01-10 14:17:07.013300: \n",
      "epoch:  40\n",
      "2024-01-10 14:25:04.372859: train loss : -0.8773\n",
      "2024-01-10 14:25:38.384941: validation loss: -0.8384\n",
      "2024-01-10 14:25:38.385498: Average global foreground Dice: [0.8876, 0.9467, 0.9316]\n",
      "2024-01-10 14:25:38.385843: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:25:39.132439: lr: 0.002137\n",
      "2024-01-10 14:25:39.132638: This epoch took 512.119212 s\n",
      "\n",
      "2024-01-10 14:25:39.132732: \n",
      "epoch:  41\n",
      "2024-01-10 14:33:37.068175: train loss : -0.8772\n",
      "2024-01-10 14:34:11.106615: validation loss: -0.8372\n",
      "2024-01-10 14:34:11.107112: Average global foreground Dice: [0.8865, 0.9462, 0.9312]\n",
      "2024-01-10 14:34:11.107214: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:34:11.774975: lr: 0.001922\n",
      "2024-01-10 14:34:11.775153: This epoch took 512.642332 s\n",
      "\n",
      "2024-01-10 14:34:11.775246: \n",
      "epoch:  42\n",
      "2024-01-10 14:42:09.847086: train loss : -0.8773\n",
      "2024-01-10 14:42:43.737361: validation loss: -0.8393\n",
      "2024-01-10 14:42:43.737873: Average global foreground Dice: [0.8898, 0.9462, 0.9314]\n",
      "2024-01-10 14:42:43.737983: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:42:44.407969: lr: 0.001704\n",
      "2024-01-10 14:42:44.408157: This epoch took 512.632829 s\n",
      "\n",
      "2024-01-10 14:42:44.408244: \n",
      "epoch:  43\n",
      "2024-01-10 14:50:41.609997: train loss : -0.8783\n",
      "2024-01-10 14:51:15.488281: validation loss: -0.8389\n",
      "2024-01-10 14:51:15.488911: Average global foreground Dice: [0.8894, 0.9462, 0.9314]\n",
      "2024-01-10 14:51:15.489059: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:51:16.259075: lr: 0.001483\n",
      "2024-01-10 14:51:16.259267: This epoch took 511.850938 s\n",
      "\n",
      "2024-01-10 14:51:16.259357: \n",
      "epoch:  44\n",
      "2024-01-10 14:59:13.716408: train loss : -0.8790\n",
      "2024-01-10 14:59:47.609102: validation loss: -0.8370\n",
      "2024-01-10 14:59:47.609812: Average global foreground Dice: [0.8877, 0.9461, 0.9306]\n",
      "2024-01-10 14:59:47.609948: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 14:59:48.319244: lr: 0.001259\n",
      "2024-01-10 14:59:48.319485: This epoch took 512.060040 s\n",
      "\n",
      "2024-01-10 14:59:48.319621: \n",
      "epoch:  45\n",
      "2024-01-10 15:07:45.360232: train loss : -0.8786\n",
      "2024-01-10 15:08:19.288021: validation loss: -0.8401\n",
      "2024-01-10 15:08:19.288520: Average global foreground Dice: [0.8879, 0.9465, 0.932]\n",
      "2024-01-10 15:08:19.288626: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 15:08:20.108176: lr: 0.00103\n",
      "2024-01-10 15:08:20.108367: This epoch took 511.788632 s\n",
      "\n",
      "2024-01-10 15:08:20.108457: \n",
      "epoch:  46\n",
      "2024-01-10 15:16:18.417526: train loss : -0.8789\n",
      "2024-01-10 15:16:52.340063: validation loss: -0.8388\n",
      "2024-01-10 15:16:52.340625: Average global foreground Dice: [0.8868, 0.9471, 0.9319]\n",
      "2024-01-10 15:16:52.340745: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 15:16:53.148044: lr: 0.000795\n",
      "2024-01-10 15:16:53.148322: This epoch took 513.039778 s\n",
      "\n",
      "2024-01-10 15:16:53.148473: \n",
      "epoch:  47\n",
      "2024-01-10 15:24:50.698984: train loss : -0.8793\n",
      "2024-01-10 15:25:24.535275: validation loss: -0.8406\n",
      "2024-01-10 15:25:24.535886: Average global foreground Dice: [0.8923, 0.9463, 0.931]\n",
      "2024-01-10 15:25:24.536006: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 15:25:25.271127: lr: 0.000552\n",
      "2024-01-10 15:25:25.271475: This epoch took 512.122859 s\n",
      "\n",
      "2024-01-10 15:25:25.271591: \n",
      "epoch:  48\n",
      "2024-01-10 15:33:22.510223: train loss : -0.8801\n",
      "2024-01-10 15:33:56.428289: validation loss: -0.8370\n",
      "2024-01-10 15:33:56.428890: Average global foreground Dice: [0.8845, 0.9466, 0.9314]\n",
      "2024-01-10 15:33:56.429003: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 15:33:57.099076: lr: 0.000296\n",
      "2024-01-10 15:33:57.099272: This epoch took 511.827584 s\n",
      "\n",
      "2024-01-10 15:33:57.099365: \n",
      "epoch:  49\n",
      "2024-01-10 15:41:54.247199: train loss : -0.8799\n",
      "2024-01-10 15:42:28.155740: validation loss: -0.8412\n",
      "2024-01-10 15:42:28.156359: Average global foreground Dice: [0.8917, 0.9472, 0.932]\n",
      "2024-01-10 15:42:28.156477: (interpret this as an estimate for the Dice of the different classes. This is not exact.)\n",
      "2024-01-10 15:42:28.879334: lr: 0.0\n",
      "2024-01-10 15:42:28.879522: saving scheduled checkpoint file...\n",
      "2024-01-10 15:42:28.910832: saving checkpoint...\n",
      "2024-01-10 15:42:29.169065: done, saving took 0.29 seconds\n",
      "2024-01-10 15:42:29.170056: done\n",
      "2024-01-10 15:42:29.170221: This epoch took 512.070488 s\n",
      "\n",
      "2024-01-10 15:42:29.189273: saving checkpoint...\n",
      "2024-01-10 15:42:29.813385: done, saving took 0.64 seconds\n",
      "IBSR_02 (2, 111, 131, 143)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "IBSR_03 (2, 116, 134, 151)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "IBSR_07 (2, 109, 139, 144)\n",
      "debug: mirroring True mirror_axes (0, 1)\n",
      "force_separate_z: None interpolation order: 1\n",
      "no resampling necessary\n",
      "force_separate_z: None interpolation order: 1\n",
      "separate z: False lowres axis None\n",
      "no separate z, order 1\n",
      "2024-01-10 15:42:55.641842: finished prediction\n",
      "2024-01-10 15:42:55.642265: evaluation of raw predictions\n",
      "2024-01-10 15:42:56.873083: determining postprocessing\n",
      "Foreground vs background\n",
      "before: 0.9200258144098021\n",
      "after:  0.9200256385476764\n",
      "1\n",
      "before: 0.8776299732195331\n",
      "after:  0.7865509515672988\n",
      "2\n",
      "before: 0.9490653332501201\n",
      "after:  0.9491035700574305\n",
      "Removing all but the largest region for class 2 improved results!\n",
      "min_valid_object_sizes None\n",
      "3\n",
      "before: 0.9333821367597531\n",
      "after:  0.8999869982286168\n",
      "done\n",
      "for which classes:\n",
      "[2]\n",
      "min_object_sizes\n",
      "None\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_train 2d nnUNetTrainerV2_Fast Task975_BrainSegmentation 5 --npz -c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of the Validation Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\n",
      "If you have questions or suggestions, feel free to open an issue at https://github.com/MIC-DKFZ/nnUNet\n",
      "\n",
      "\n",
      "I will now ensemble combinations of the following models:\n",
      " ['2d']\n",
      "2d 0.929296028022764\n",
      "Task975_BrainSegmentation submit model 2d 0.929296028022764\n",
      "\n",
      "Here is how you should predict test cases. Run in sequential order and replace all input and output folder names with your personalized ones\n",
      "\n",
      "nnUNet_predict -i FOLDER_WITH_TEST_CASES -o OUTPUT_FOLDER_MODEL1 -tr nnUNetTrainerV2_Fast -ctr nnUNetTrainerV2CascadeFullRes -m 2d -p nnUNetPlansv2.1 -t Task975_BrainSegmentation\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_find_best_configuration -m 2d -t 975 -tr nnUNetTrainerV2_Fast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n",
      "['IBSR_08', 'IBSR_12', 'IBSR_01']\n",
      "Results for validation IBSR_08 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 27.9464 GM: 7.2284 WM: 6.7082\n",
      "Average Volume Distance\n",
      "CFS: 1152.0000 GM: 25357.5000 WM: 14385.0000\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.0592 GM: -0.0296 WM: 0.0275\n",
      "Dice Score\n",
      "CFS: 0.9290 GM: 0.9438 WM: 0.9436\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_12 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 15.8430 GM: 6.7082 WM: 6.7268\n",
      "Average Volume Distance\n",
      "CFS: 3030.0000 GM: 544.5000 WM: 9462.0000\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.1537 GM: 0.0006 WM: -0.0216\n",
      "Dice Score\n",
      "CFS: 0.8877 GM: 0.9444 WM: 0.9465\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_01 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 36.7543 GM: 12.3366 WM: 8.7464\n",
      "Average Volume Distance\n",
      "CFS: 27.6855 GM: 6755.2734 WM: 630.1758\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0031 GM: 0.0085 WM: -0.0017\n",
      "Dice Score\n",
      "CFS: 0.8612 GM: 0.9549 WM: 0.9399\n",
      "----------------------------------------------------------\n",
      "Fold 1\n",
      "['IBSR_04', 'IBSR_09', 'IBSR_07']\n",
      "Results for validation IBSR_04 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 5.9322 GM: 7.5584 WM: 9.0000\n",
      "Average Volume Distance\n",
      "CFS: 516.7969 GM: 39136.8164 WM: 7497.5098\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.0293 GM: -0.0474 WM: 0.0170\n",
      "Dice Score\n",
      "CFS: 0.9031 GM: 0.9404 WM: 0.9357\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_09 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 6.2022 GM: 8.2799 WM: 7.8031\n",
      "Average Volume Distance\n",
      "CFS: 794.5470 GM: 8077.8944 WM: 6082.0681\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.0680 GM: -0.0080 WM: -0.0135\n",
      "Dice Score\n",
      "CFS: 0.8718 GM: 0.9606 WM: 0.9444\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_07 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 8.5440 GM: 7.0000 WM: 8.0000\n",
      "Average Volume Distance\n",
      "CFS: 2443.5000 GM: 5158.5000 WM: 12000.0000\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0704 GM: -0.0071 WM: -0.0271\n",
      "Dice Score\n",
      "CFS: 0.9179 GM: 0.9451 WM: 0.9508\n",
      "----------------------------------------------------------\n",
      "Fold 2\n",
      "['IBSR_15', 'IBSR_11', 'IBSR_13']\n",
      "Results for validation IBSR_15 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 12.9946 GM: 8.0845 WM: 9.6498\n",
      "Average Volume Distance\n",
      "CFS: 920.6656 GM: 2830.3109 WM: 7537.6866\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0341 GM: 0.0027 WM: 0.0157\n",
      "Dice Score\n",
      "CFS: 0.9404 GM: 0.9562 WM: 0.9390\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_11 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 11.8004 GM: 9.0000 WM: 7.5000\n",
      "Average Volume Distance\n",
      "CFS: 46.5000 GM: 30147.0000 WM: 18322.5000\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0029 GM: 0.0398 WM: -0.0333\n",
      "Dice Score\n",
      "CFS: 0.9025 GM: 0.9462 WM: 0.9532\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_13 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 9.7878 GM: 8.0647 WM: 9.2333\n",
      "Average Volume Distance\n",
      "CFS: 395.5078 GM: 20638.9160 WM: 23265.0879\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.0279 GM: 0.0220 WM: -0.0587\n",
      "Dice Score\n",
      "CFS: 0.8868 GM: 0.9541 WM: 0.9223\n",
      "----------------------------------------------------------\n",
      "Fold 3\n",
      "['IBSR_05', 'IBSR_10', 'IBSR_14']\n",
      "Results for validation IBSR_05 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 7.3962 GM: 7.8750 WM: 7.7058\n",
      "Average Volume Distance\n",
      "CFS: 1414.5996 GM: 8441.4551 WM: 22313.2324\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.0488 GM: -0.0110 WM: -0.0427\n",
      "Dice Score\n",
      "CFS: 0.9318 GM: 0.9302 WM: 0.9362\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_10 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 16.1845 GM: 11.9930 WM: 8.8150\n",
      "Average Volume Distance\n",
      "CFS: 1226.5031 GM: 33007.3318 WM: 17886.7662\n",
      "Relative Absolute Volume Distance\n",
      "CFS: 0.0525 GM: -0.0290 WM: 0.0342\n",
      "Dice Score\n",
      "CFS: 0.9167 GM: 0.9599 WM: 0.9460\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_14 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 6.0029 GM: 7.2957 WM: 6.0930\n",
      "Average Volume Distance\n",
      "CFS: 947.9004 GM: 25127.9297 WM: 11249.5605\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0493 GM: -0.0267 WM: 0.0236\n",
      "Dice Score\n",
      "CFS: 0.9283 GM: 0.9582 WM: 0.9479\n",
      "----------------------------------------------------------\n",
      "Fold 4\n",
      "['IBSR_06', 'IBSR_02', 'IBSR_03']\n",
      "Results for validation IBSR_06 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 14.6373 GM: 6.1847 WM: 7.7621\n",
      "Average Volume Distance\n",
      "CFS: 477.0000 GM: 12759.0000 WM: 19147.5000\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0301 GM: -0.0173 WM: 0.0436\n",
      "Dice Score\n",
      "CFS: 0.9090 GM: 0.9484 WM: 0.9500\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_02 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 30.2021 GM: 6.9678 WM: 9.4942\n",
      "Average Volume Distance\n",
      "CFS: 94.9219 GM: 13097.9004 WM: 2747.4609\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0107 GM: -0.0161 WM: 0.0073\n",
      "Dice Score\n",
      "CFS: 0.8447 GM: 0.9533 WM: 0.9336\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_03 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 16.1032 GM: 7.8750 WM: 7.1986\n",
      "Average Volume Distance\n",
      "CFS: 651.2695 GM: 28717.8223 WM: 31147.5586\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0580 GM: -0.0304 WM: 0.0787\n",
      "Dice Score\n",
      "CFS: 0.8692 GM: 0.9509 WM: 0.9203\n",
      "----------------------------------------------------------\n",
      "Fold 5\n",
      "['IBSR_02', 'IBSR_03', 'IBSR_07']\n",
      "Results for validation IBSR_02 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 30.2021 GM: 6.7108 WM: 9.5404\n",
      "Average Volume Distance\n",
      "CFS: 18.4570 GM: 10123.6816 WM: 1645.3125\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0021 GM: -0.0125 WM: 0.0044\n",
      "Dice Score\n",
      "CFS: 0.8450 GM: 0.9527 WM: 0.9320\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_03 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 16.1032 GM: 7.3197 WM: 7.7058\n",
      "Average Volume Distance\n",
      "CFS: 263.6719 GM: 31862.1094 WM: 35061.7676\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0243 GM: -0.0336 WM: 0.0895\n",
      "Dice Score\n",
      "CFS: 0.8696 GM: 0.9504 WM: 0.9186\n",
      "----------------------------------------------------------\n",
      "Results for validation IBSR_07 with 2D U-net\n",
      "Haussdorf Distance\n",
      "CFS: 8.5440 GM: 7.0000 WM: 8.0000\n",
      "Average Volume Distance\n",
      "CFS: 2598.0000 GM: 252.0000 WM: 14953.5000\n",
      "Relative Absolute Volume Distance\n",
      "CFS: -0.0746 GM: -0.0004 WM: -0.0336\n",
      "Dice Score\n",
      "CFS: 0.9183 GM: 0.9443 WM: 0.9496\n",
      "----------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in range(6):\n",
    "  preds_path_2d = f'nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_{i}/validation_raw_postprocessed'\n",
    "  gt_path_2d = 'nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/gt_niftis'\n",
    "\n",
    "  # List all files in the folder\n",
    "  file_list = os.listdir(preds_path_2d)\n",
    "  val_list = [file.replace(\".nii.gz\", \"\") for file in file_list if file.endswith(\".nii.gz\")]\n",
    "  print(f'Fold {i}')\n",
    "  print(val_list)\n",
    "  for img_id in val_list:\n",
    "    gt = sitk.ReadImage(f'{gt_path_2d}/{img_id}.nii.gz')\n",
    "    vspace = gt.GetSpacing()\n",
    "    gt = sitk.GetArrayFromImage(gt)\n",
    "    pred = sitk.GetArrayFromImage(sitk.ReadImage(f'{preds_path_2d}/{img_id}.nii.gz'))\n",
    "    HF = haussdorf(gt, pred, vspace)\n",
    "    AVD = avd(gt, pred, vspace)\n",
    "    RAV = rel_abs_vol_dif(gt, pred)\n",
    "    DICE = dice_score(gt, pred)\n",
    "    print(f'Results for validation {img_id} with 2D U-net')\n",
    "    print(f'Haussdorf Distance')\n",
    "    print(f'CFS: {HF[0]:.4f} GM: {HF[1]:.4f} WM: {HF[2]:.4f}')\n",
    "    print(f'Average Volume Distance')\n",
    "    print(f'CFS: {AVD[0]:.4f} GM: {AVD[1]:.4f} WM: {AVD[2]:.4f}')\n",
    "    print(f'Relative Absolute Volume Distance')\n",
    "    print(f'CFS: {RAV[0]:.4f} GM: {RAV[1]:.4f} WM: {RAV[2]:.4f}')\n",
    "    print(f'Dice Score')\n",
    "    print(f'CFS: {DICE[0]:.4f} GM: {DICE[1]:.4f} WM: {DICE[2]:.4f}')\n",
    "    print('----------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Please cite the following paper when using nnUNet:\n",
      "\n",
      "Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. \"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.\" Nat Methods (2020). https://doi.org/10.1038/s41592-020-01008-z\n",
      "\n",
      "\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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1\n",
      "This model expects 1 input modalities for each image\n",
      "Found 3 unique case ids, here are some examples: ['IBSR_16' 'IBSR_16' 'IBSR_16']\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: 3\n",
      "number of cases that still need to be predicted: 0\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/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_0', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_1', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_2', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_3', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_4', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_5']\n",
      "using the following model files:  ['/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_0/model_final_checkpoint.model', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_1/model_final_checkpoint.model', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_2/model_final_checkpoint.model', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_3/model_final_checkpoint.model', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_4/model_final_checkpoint.model', '/home/edalita/Documents/MAIA/3-Semestre/MIRMISAProject/IBSR18-tissue-segmentation/dl_part/nnUNet_results/nnUNet/2d/Task975_BrainSegmentation/nnUNetTrainerV2_Fast__nnUNetPlansv2.1/fold_5/model_final_checkpoint.model']\n",
      "starting preprocessing generator\n",
      "starting prediction...\n",
      "inference done. Now waiting for the segmentation export to finish...\n",
      "postprocessing...\n"
     ]
    }
   ],
   "source": [
    "! nnUNet_predict -i nnUNet_raw/nnUNet_raw_data/Task975_BrainSegmentation/imagesTs -o task975_Results -tr nnUNetTrainerV2_Fast -ctr nnUNetTrainerV2CascadeFullRes -m 2d -p nnUNetPlansv2.1 -t Task975_BrainSegmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Image Shape:  (256, 128, 10, 1)\n",
      "Predicted Image Shape: (256, 128, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x800 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_brain()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "projectMisaMira",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}