MLI-CW-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CO416 - Machine Learning for  Imaging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coursework 2 - Age regression from brain MRI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting age from a brain MRI scan can have diagnostic value for a number of diseases that cause structural changes and damage to the brain. Discrepancy between the predicted, biological age and the real, chronological age of a patient might indicate the presence of disease and abnormal changes to the brain. For this we need an accurate predictor of brain age which may be learned from a set of healthy reference subjects.\n",
    "The objective for the coursework is to implement two different supervised learning approaches for age regression from brain MRI. Data from 600 healthy subjects will be provided. Each approach will require a processing pipeline with different components that you will need to implement using methods that were discussed in the lectures and tutorials. There are dedicated sections in the Jupyter notebook for each approach which contain some detailed instructions, hints and notes.\n",
    "\n",
    "You may find useful ideas and implementations in the tutorial notebooks. Make sure to add documentation to your code. Markers will find it easier to understand your reasoning when sufficiently detailed comments are provided in your implementations.\n",
    "\n",
    "#### Read the descriptions and provided code cells carefully and look out for the cells marked with 'TASK'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting started and familiarise ourselves with the data\n",
    "\n",
    "The following cells provide some helper functions to load the data, and provide some overview and visualisation of the statistics over the population of 600 subjects. Let's start by loading the meta data, that is the data containing information about the subject IDs, their age, and gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_code</th>\n",
       "      <th>gender_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CC110033</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CC110037</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC110045</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC110056</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CC110062</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  age  gender_code gender_text\n",
       "0  CC110033   24            1        MALE\n",
       "1  CC110037   18            1        MALE\n",
       "2  CC110045   24            2      FEMALE\n",
       "3  CC110056   22            2      FEMALE\n",
       "4  CC110062   20            1        MALE"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the meta data using pandas\n",
    "import pandas as pd\n",
    "\n",
    "data_dir = \"./data/brain/\"\n",
    "\n",
    "meta_data = pd.read_csv(data_dir + 'meta/clean_participant_data.csv')\n",
    "meta_data.head() # show the first five data entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at some population statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.catplot(x=\"gender_text\", data=meta_data, kind=\"count\")\n",
    "plt.title('Gender distribution')\n",
    "plt.xlabel('Gender')\n",
    "plt.show()\n",
    "\n",
    "sns.distplot(meta_data['age'], bins=[10,20,30,40,50,60,70,80,90])\n",
    "plt.title('Age distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(range(len(meta_data['age'])),meta_data['age'], marker='.')\n",
    "plt.grid()\n",
    "plt.xlabel('Subject')\n",
    "plt.ylabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up a simple medical image viewer and import SimpleITK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import SimpleITK as sitk\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ipywidgets import interact, fixed\n",
    "from IPython.display import display\n",
    "\n",
    "from utils.image_viewer import display_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imaging data\n",
    "\n",
    "Let's check out the imaging data that is available for each subject. This cell also shows how to retrieve data given a particular subject ID from the meta data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imaging data of subject CC110033 with age 24\n",
      "\n",
      "MR Image (used in part A)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brain mask (used in part A)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spatially normalised grey matter maps (used in part B)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "# Subject with index 0\n",
    "ID = meta_data['ID'][0]\n",
    "age = meta_data['age'][0]\n",
    "\n",
    "# Data folders\n",
    "image_dir = data_dir + 'images/'\n",
    "image_filenames = glob.glob(image_dir + '*.nii.gz')\n",
    "\n",
    "mask_dir = data_dir + 'masks/'\n",
    "mask_filenames = glob.glob(mask_dir + '*.nii.gz')\n",
    "\n",
    "greymatter_dir = data_dir + 'greymatter/'\n",
    "greymatter_filenames = glob.glob(greymatter_dir + '*.nii.gz')\n",
    "\n",
    "\n",
    "image_filename = [f for f in image_filenames if ID in f][0]\n",
    "img = sitk.ReadImage(image_filename)\n",
    "\n",
    "mask_filename = [f for f in mask_filenames if ID in f][0]\n",
    "msk = sitk.ReadImage(mask_filename)\n",
    "\n",
    "greymatter_filename = [f for f in greymatter_filenames if ID in f][0]\n",
    "gm = sitk.ReadImage(greymatter_filename)\n",
    "\n",
    "print('Imaging data of subject ' + ID + ' with age ' + str(age))\n",
    "\n",
    "print('\\nMR Image (used in part A)')\n",
    "display_image(img, window=400, level=200)\n",
    "\n",
    "print('Brain mask (used in part A)')\n",
    "display_image(msk)\n",
    "\n",
    "print('Spatially normalised grey matter maps (used in part B)')\n",
    "display_image(gm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part A: Volume-based regression using brain structure segmentation\n",
    "\n",
    "The first approach aims to regress the age of a subject using the volumes of brain tissues as features. The structures include grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF). It is known that with increasing age the ventricles enlarge (filled with CSF), while it is assumed that grey and white matter volume may decrease over time. However, as overall brain volume varies across individuals, taking the absolute volumes of tissues might not be predictive. Instead, relative volumes need to be computed as the ratios between each tissue volume and overall brain volume. To this end, a four-class (GM, WM, CSF, and background) brain segmentation needs to be implemented and applied to the 600 brain scans. Brain masks are provided which have been generated with a state-of-the-art neuroimaging brain extraction tool.\n",
    "\n",
    "Different regression techniques should be explored, and it might be beneficial to investigate what the best set of features is for this task. Are all volume features equally useful, or is it even better to combine some of them and create new features. How does a simple linear regression perform compared to a model with higher order polynomials? Do you need regularisation? How about other regression methods such as regression trees or neural networks? The accuracy of different methods should be evaluated using two-fold cross-validation, and average age prediction accuracy should be compared and reported appropriately.\n",
    "\n",
    "*Note:* For part A, only the MR images and the brain masks should be used from the imaging data. The spatially normalised grey matter maps are used in part B only. If you struggle with task A-1, you can continue with A-2 using the provided reference segmentations in subfolder `segs_refs`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK A-1: Brain tissue segmentation\n",
    "\n",
    "Implement a CNN model for brain tissue segmentation which can provide segmentations of GM, WM, and CSF. For this task (and only for this task), we provide a separate dataset of 52 subjects which are split into 47 images for training and 5 for validation. The template code below has the data handling and main training routines already implemented, so you can focus on implementing a suitable CNN model. A simple model is provided, but this won't perform very well.\n",
    "\n",
    "Once your model is trained and you are happy with the results on the validation data you should apply it to the 600 test images. We provide reference segmentations in a subfolder `segs_refs` for all subjects. Calculate Dice similarity coefficients per tissue when comparing your predicted segmentations for the 600 test images to the reference segmentations. Summarise the statistics of the 600 Dice scores for each tissue class in [box-and-whisker-plots](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.boxplot.html).\n",
    "\n",
    "*Note:* Implementing a full-fledged machine learning pipeline with training and testing procedures in Jupyter notebooks is a bit cumbersome and a pain to debug. Also, running bigger training tasks can be unstable. The code below should work as is on your VM. However, if you want to get a bit more serious about implementing an advanced CNN approach for image segmentation, you may want to move code into separate Python scripts and run them from the terminal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from utils.data_helper import ImageSegmentationDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check that the GPU is up and running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda:0\n",
      "GPU: Tesla K80\n"
     ]
    }
   ],
   "source": [
    "cuda_dev = '0' #GPU device 0 (can be changed if multiple GPUs are available)\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:\" + cuda_dev if use_cuda else \"cpu\")\n",
    "\n",
    "print('Device: ' + str(device))\n",
    "if use_cuda:\n",
    "    print('GPU: ' + str(torch.cuda.get_device_name(int(cuda_dev))))        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Config and hyper-parameters\n",
    "\n",
    "Here we set some default hyper-parameters and a starting configuration for the image resolution and others.\n",
    "\n",
    "**This needs to be revisited to optimise these values. In particular, you may want to run your final model on higher resolution images.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_seed = 42 #fixed random seed\n",
    "\n",
    "img_size = [64, 64, 64]\n",
    "img_spacing = [3, 3, 3]\n",
    "\n",
    "num_epochs = 200\n",
    "learning_rate = 0.001\n",
    "batch_size = 2\n",
    "val_interval = 10\n",
    "\n",
    "num_classes = 4\n",
    "\n",
    "out_dir = './output'\n",
    "\n",
    "# Create output directory\n",
    "if not os.path.exists(out_dir):\n",
    "    os.makedirs(out_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading and pre-processing of training and validation data\n",
    "\n",
    "We apply some standard pre-processing on the data such as intensity normalization (zero mean unit variance) and downsampling according to the configuration above.\n",
    "\n",
    "**We provide a 'debug' csv file pointing to just a few images for training. Replace this with the full training dataset when you train your full model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOADING TRAINING DATA...\n",
      "+ reading image msub-CC110319_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC110319.nii.gz\n",
      "+ reading mask sub-CC110319_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC120208_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC120208.nii.gz\n",
      "+ reading mask sub-CC120208_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC120462_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC120462.nii.gz\n",
      "+ reading mask sub-CC120462_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC121144_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC121144.nii.gz\n",
      "+ reading mask sub-CC121144_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC122405_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC122405.nii.gz\n",
      "+ reading mask sub-CC122405_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC210422_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC210422.nii.gz\n",
      "+ reading mask sub-CC210422_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC220203_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC220203.nii.gz\n",
      "+ reading mask sub-CC220203_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC220518_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC220518.nii.gz\n",
      "+ reading mask sub-CC220518_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC221220_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC221220.nii.gz\n",
      "+ reading mask sub-CC221220_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC221595_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC221595.nii.gz\n",
      "+ reading mask sub-CC221595_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC222120_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC222120.nii.gz\n",
      "+ reading mask sub-CC222120_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC222956_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC222956.nii.gz\n",
      "+ reading mask sub-CC222956_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC310203_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC310203.nii.gz\n",
      "+ reading mask sub-CC310203_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC310407_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC310407.nii.gz\n",
      "+ reading mask sub-CC310407_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC320089_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC320089.nii.gz\n",
      "+ reading mask sub-CC320089_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC320336_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC320336.nii.gz\n",
      "+ reading mask sub-CC320336_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC320574_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC320574.nii.gz\n",
      "+ reading mask sub-CC320574_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC321069_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC321069.nii.gz\n",
      "+ reading mask sub-CC321069_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC321428_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC321428.nii.gz\n",
      "+ reading mask sub-CC321428_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC321899_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC321899.nii.gz\n",
      "+ reading mask sub-CC321899_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC410113_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC410113.nii.gz\n",
      "+ reading mask sub-CC410113_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC410243_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC410243.nii.gz\n",
      "+ reading mask sub-CC410243_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC410432_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC410432.nii.gz\n",
      "+ reading mask sub-CC410432_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC420137_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC420137.nii.gz\n",
      "+ reading mask sub-CC420137_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC420202_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC420202.nii.gz\n",
      "+ reading mask sub-CC420202_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC420286_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC420286.nii.gz\n",
      "+ reading mask sub-CC420286_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC420888_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC420888.nii.gz\n",
      "+ reading mask sub-CC420888_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC510226_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC510226.nii.gz\n",
      "+ reading mask sub-CC510226_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC510329_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC510329.nii.gz\n",
      "+ reading mask sub-CC510329_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC510474_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC510474.nii.gz\n",
      "+ reading mask sub-CC510474_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC520002_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC520002.nii.gz\n",
      "+ reading mask sub-CC520002_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC520134_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC520134.nii.gz\n",
      "+ reading mask sub-CC520134_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC520253_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC520253.nii.gz\n",
      "+ reading mask sub-CC520253_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC520503_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC520503.nii.gz\n",
      "+ reading mask sub-CC520503_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC520775_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC520775.nii.gz\n",
      "+ reading mask sub-CC520775_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC610288_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC610288.nii.gz\n",
      "+ reading mask sub-CC610288_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC610575_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC610575.nii.gz\n",
      "+ reading mask sub-CC610575_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC620073_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC620073.nii.gz\n",
      "+ reading mask sub-CC620073_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC620262_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC620262.nii.gz\n",
      "+ reading mask sub-CC620262_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC620444_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC620444.nii.gz\n",
      "+ reading mask sub-CC620444_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC620557_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC620557.nii.gz\n",
      "+ reading mask sub-CC620557_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC620821_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC620821.nii.gz\n",
      "+ reading mask sub-CC620821_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC621642_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC621642.nii.gz\n",
      "+ reading mask sub-CC621642_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC710416_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC710416.nii.gz\n",
      "+ reading mask sub-CC710416_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC720103_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC720103.nii.gz\n",
      "+ reading mask sub-CC720103_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC720511_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC720511.nii.gz\n",
      "+ reading mask sub-CC720511_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC721291_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC721291.nii.gz\n",
      "+ reading mask sub-CC721291_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "\n",
      "LOADING VALIDATION DATA...\n",
      "+ reading image msub-CC220901_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC220901.nii.gz\n",
      "+ reading mask sub-CC220901_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC320698_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC320698.nii.gz\n",
      "+ reading mask sub-CC320698_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC420454_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC420454.nii.gz\n",
      "+ reading mask sub-CC420454_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC610058_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC610058.nii.gz\n",
      "+ reading mask sub-CC610058_T1w_rigid_to_mni_brain_mask.nii.gz\n",
      "+ reading image msub-CC710679_T1w_rigid_to_mni.nii.gz\n",
      "+ reading segmentation CC710679.nii.gz\n",
      "+ reading mask sub-CC710679_T1w_rigid_to_mni_brain_mask.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# USE THIS FOR TRAINING ON ALL 47 SUBJECTS\n",
    "train_data = data_dir + 'train/csv/train.csv'\n",
    "\n",
    "# USE THIS FOR DEBUGGING WITH JUST 2 SUBJECTS\n",
    "#train_data = data_dir + 'train/csv/train_debug.csv'\n",
    "\n",
    "val_data = data_dir + 'train/csv/val.csv'\n",
    "\n",
    "print('LOADING TRAINING DATA...')\n",
    "dataset_train = ImageSegmentationDataset(train_data, img_spacing, img_size)\n",
    "dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "print('\\nLOADING VALIDATION DATA...')\n",
    "dataset_val = ImageSegmentationDataset(val_data, img_spacing, img_size)\n",
    "dataloader_val = torch.utils.data.DataLoader(dataset_val, batch_size=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualise training example\n",
    "\n",
    "Just to check how a training image looks like after pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: msub-CC110319_T1w_rigid_to_mni.nii.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation: CC110319.nii.gz\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mask\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = dataset_train.get_sample(0)\n",
    "img_name = dataset_train.get_img_name(0)\n",
    "seg_name = dataset_train.get_seg_name(0)\n",
    "print('Image: ' + img_name)\n",
    "display_image(sample['img'], window=5, level=0)\n",
    "print('Segmentation: ' + seg_name)\n",
    "display_image(sitk.LabelToRGB(sample['seg']))\n",
    "print('Mask')\n",
    "display_image(sample['msk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Model\n",
    "\n",
    "This is the **key part of task A-1** where you have to design a suitable CNN model for brain segmentation. The simple model provided below works to some degree (it let's you run through the upcoming cells), but it will not perform very well. Use what you learned in the lectures to come up with a good architecture. Start with a simple, shallow model and only increase complexity (e.g., number of layers) if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleNet3D(nn.Module):\n",
    "\n",
    "    def __init__(self, num_classes):\n",
    "        super(SimpleNet3D, self).__init__()\n",
    "        self.conv1 = nn.Conv3d(1, 16, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, padding=1)\n",
    "        self.conv3 = nn.Conv3d(32, 16, kernel_size=3, padding=1)\n",
    "        self.conv4 = nn.Conv3d(16, num_classes, kernel_size=3, padding=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = self.conv4(x)\n",
    "        return F.softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "def passthrough(x, **kwargs):\n",
    "    return x\n",
    "\n",
    "def ELUCons(elu, nchan):\n",
    "    if elu:\n",
    "        return nn.ELU(inplace=True)\n",
    "    else:\n",
    "        return nn.PReLU(nchan)\n",
    "\n",
    "# normalization between sub-volumes is necessary\n",
    "# for good performance\n",
    "class ContBatchNorm3d(nn.modules.batchnorm._BatchNorm):\n",
    "    def _check_input_dim(self, x):\n",
    "        if x.dim() != 5:\n",
    "            raise ValueError('expected 5D input (got {}D input)'\n",
    "                             .format(x.dim()))\n",
    "        #super(ContBatchNorm3d, self)._check_input_dim(x)\n",
    "\n",
    "    def forward(self, x):\n",
    "        self._check_input_dim(x)\n",
    "        return F.batch_norm(\n",
    "            x, self.running_mean, self.running_var, self.weight, self.bias,\n",
    "            True, self.momentum, self.eps)\n",
    "\n",
    "\n",
    "class LUConv(nn.Module):\n",
    "    def __init__(self, nchan, elu):\n",
    "        super(LUConv, self).__init__()\n",
    "        self.relu1 = ELUCons(elu, nchan)\n",
    "        self.conv1 = nn.Conv3d(nchan, nchan, kernel_size=5, padding=2)\n",
    "        self.bn1 = ContBatchNorm3d(nchan)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.relu1(self.bn1(self.conv1(x)))\n",
    "        return out\n",
    "\n",
    "\n",
    "def _make_nConv(nchan, depth, elu):\n",
    "    layers = []\n",
    "    for _ in range(depth):\n",
    "        layers.append(LUConv(nchan, elu))\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "class InputTransition(nn.Module):\n",
    "    def __init__(self, outChans, elu):\n",
    "        super(InputTransition, self).__init__()\n",
    "        self.conv1 = nn.Conv3d(1, 16, kernel_size=5, padding=2)\n",
    "        self.bn1 = ContBatchNorm3d(16)\n",
    "        self.relu1 = ELUCons(elu, 16)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        # split input in to 16 channels\n",
    "        x16 = torch.cat((x, x, x, x, x, x, x, x,\n",
    "                         x, x, x, x, x, x, x, x), 1)\n",
    "        #print(out.shape)\n",
    "        #print(x16.shape)\n",
    "        out = torch.add(out, x16)\n",
    "        #print(out.shape)\n",
    "        out = self.relu1(out)\n",
    "        return out\n",
    "\n",
    "\n",
    "class DownTransition(nn.Module):\n",
    "    def __init__(self, inChans, nConvs, elu, dropout=False):\n",
    "        super(DownTransition, self).__init__()\n",
    "        outChans = 2*inChans\n",
    "        self.down_conv = nn.Conv3d(inChans, outChans, kernel_size=2, stride=2)\n",
    "        self.bn1 = ContBatchNorm3d(outChans)\n",
    "        self.do1 = passthrough\n",
    "        self.relu1 = ELUCons(elu, outChans)\n",
    "        self.relu2 = ELUCons(elu, outChans)\n",
    "        if dropout:\n",
    "            self.do1 = nn.Dropout3d()\n",
    "        self.ops = _make_nConv(outChans, nConvs, elu)\n",
    "\n",
    "    def forward(self, x):\n",
    "        down = self.relu1(self.bn1(self.down_conv(x)))\n",
    "        out = self.do1(down)\n",
    "        out = self.ops(out)\n",
    "        out = self.relu2(torch.add(out, down))\n",
    "        return out\n",
    "\n",
    "\n",
    "class UpTransition(nn.Module):\n",
    "    def __init__(self, inChans, outChans, nConvs, elu, dropout=False):\n",
    "        super(UpTransition, self).__init__()\n",
    "        self.up_conv = nn.ConvTranspose3d(inChans, outChans // 2, kernel_size=2, stride=2)\n",
    "        self.bn1 = ContBatchNorm3d(outChans // 2)\n",
    "        self.do1 = passthrough\n",
    "        self.do2 = nn.Dropout3d()\n",
    "        self.relu1 = ELUCons(elu, outChans // 2)\n",
    "        self.relu2 = ELUCons(elu, outChans)\n",
    "        if dropout:\n",
    "            self.do1 = nn.Dropout3d()\n",
    "        self.ops = _make_nConv(outChans, nConvs, elu)\n",
    "\n",
    "    def forward(self, x, skipx):\n",
    "        out = self.do1(x)\n",
    "        skipxdo = self.do2(skipx)\n",
    "        out = self.relu1(self.bn1(self.up_conv(out)))\n",
    "        xcat = torch.cat((out, skipxdo), 1)\n",
    "        out = self.ops(xcat)\n",
    "        out = self.relu2(torch.add(out, xcat))\n",
    "        return out\n",
    "\n",
    "\n",
    "class OutputTransition(nn.Module):\n",
    "    def __init__(self, inChans, elu, num_classes):\n",
    "        super(OutputTransition, self).__init__()\n",
    "        self.conv1 = nn.Conv3d(inChans, num_classes, kernel_size=5, padding=2)\n",
    "        self.bn1 = ContBatchNorm3d(num_classes)\n",
    "        self.conv2 = nn.Conv3d(num_classes, num_classes, kernel_size=1)\n",
    "        self.relu1 = ELUCons(elu, num_classes)\n",
    "        self.softmax = F.softmax\n",
    "\n",
    "    def forward(self, x):\n",
    "        # convolve 32 down to 2 channels\n",
    "        out = self.relu1(self.bn1(self.conv1(x)))\n",
    "        out = self.conv2(out)\n",
    "\n",
    "        # make channels the last axis\n",
    "        # out = out.permute(0, 2, 3, 4, 1).contiguous()\n",
    "        # flatten\n",
    "#         print(out[0][3])\n",
    "        # out = out.view(out.numel() // 2, 2)\n",
    "        out = self.softmax(out, dim=1)\n",
    "#         print(out.shape)\n",
    "#         print(out[0][3])\n",
    "        # treat channel 0 as the predicted output\n",
    "        return out\n",
    "\n",
    "\n",
    "class SimpleNet3D(nn.Module):\n",
    "    # the number of convolutions in each layer corresponds\n",
    "    # to what is in the actual prototxt, not the intent\n",
    "    def __init__(self, num_classes, elu=True):\n",
    "        super(SimpleNet3D, self).__init__()\n",
    "        self.in_tr = InputTransition(16, elu)\n",
    "        self.down_tr32 = DownTransition(16, 1, elu)\n",
    "        self.down_tr64 = DownTransition(32, 2, elu)\n",
    "        self.down_tr128 = DownTransition(64, 3, elu, dropout=True)\n",
    "        self.down_tr256 = DownTransition(128, 2, elu, dropout=True)\n",
    "        self.up_tr256 = UpTransition(256, 256, 2, elu, dropout=True)\n",
    "        self.up_tr128 = UpTransition(256, 128, 2, elu, dropout=True)\n",
    "        self.up_tr64 = UpTransition(128, 64, 1, elu)\n",
    "        self.up_tr32 = UpTransition(64, 32, 1, elu)\n",
    "        self.out_tr = OutputTransition(32, elu, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out16 = self.in_tr(x)\n",
    "        out32 = self.down_tr32(out16)\n",
    "        out64 = self.down_tr64(out32)\n",
    "        out128 = self.down_tr128(out64)\n",
    "        out256 = self.down_tr256(out128)\n",
    "        out = self.up_tr256(out256, out128)\n",
    "        out = self.up_tr128(out, out64)\n",
    "        out = self.up_tr64(out, out32)\n",
    "        out = self.up_tr32(out, out16)\n",
    "        out = self.out_tr(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TRAINING\n",
    "\n",
    "Below is an implementation of a full training procedure including a loop for intermediate evaluation of the model on the validation data. Feel free to modify this procedure. For example, in addition to the loss you may want to monitor precision, recall and Dice scores (or others)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START TRAINING...\n",
      "+ TRAINING \tEpoch: 1 \tLoss: 1.382734\n",
      "--------------------------------------------------\n",
      "+ VALIDATE \tEpoch: 1 \tLoss: 1.378221\n"
     ]
    },
    {
     "data": {
      "image/png": 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qjkHvn0uSCxcYmrQQ5sSMDPu/5beH1vGNfJ4cmZLGlOSDwA+AL1fVL0e43a4kB5Ic4MSJ2QUozZk5oa6ymJLGkOQcev807q2qHza7X0uypfn5FuB4v9vaYkmryJxQl1lMSSNKEnoHqT1cVd9c96NH6HUDgGl3BZBazJyYg+pz6qeL3UNawDVT0uiuAb4E/F2Sp5t9XwfuBB5IshN4Gbh1QfFJ82ZOqNMspqQRVdXP2Hg56HXzjEVqA3NCXWcxNW3jtFCZ5YisLV0kaTU5m9carpmSJEmagCNT83Cwz7CQCwQlSVoJjkxJkiRNwGJKkiRpAk7zzUrbmvsOan7cghDHdsjV9ZKkxRmqmEryEvAW8BvgZFWtJdkE3A9sB14C/qCq3phNmJIkSe00ysjU71bV6+u2dwP7q+rOJLub7dunGp1mY5lHofpxMb8kaYEmWTN1E7C3ubwXuHnycCRJkpbLsMVUAX+Z5GCSXc2+i6rqGEBzfuEsApQkSWqzYaf5rqmqV5NcCOxL8tywd9AUX70CbNu20SOUBjm1AN3pPknSAgw1MlVVrzbnx4GHgKuB15JsAWjOj29w2z1VtVZVa2zePJ2oJUmSWmJgMZXk3CQfOnUZ+D3g58AjwI7majuAh2cVpHRGV5WjUpKkhRlmmu8i4KEkp67/51X1WJIngQeS7AReBm6dXZiSJEntNLCYqqoXgSv67P9H4LpZBCVJkrQsPAK6ltP6o547xSdJWiB780mSJE3AYkoaUZLvJjme5Ofr9m1Ksi/Jkeb8gkXGKM2TOaGus5hS+x3Ke5sZn/oG32Km+L4P3PCufafaK10G7G+2pa74PuaEOsxiShpRVf0V8E/v2m17JXWWOaGucwG62undI1Hv3te+ReentVdqugX0ZVcAdYQ5oc5wZEqaM7sCSKczJ7TsLKak6RiqvZLUIeaEOsNiSu3Ub4H5YhedD7Lc7ZX6LfLXfKzu3365c0IagcWUNKIkfwH8NXB5kqNNS6U7geuTHAGub7alTjAn1HXdXIB+6lNgO0c4BK3+pF5VX9jgR8vVXqndC/pX04q+96xMTkhjcmRKkiRpAhZTkiRJE1jtaT6nMZaXz9fs9fsb95te9bmYTL/3Id+bpJXiyJQkSdIELKYkSZImsNrTfA6fS+NzSmo6+v3N/DuqK9a/f3yqed0fXL33lKFGppKcn+TBJM8lOZzkM0k2JdmX5EhzfsGsg5UkSWqbYUemvgM8VlW3JHk/8K+ArwP7q+rOJLuB3cDtM4qzu1wQrHlyFEXSNK1//6j3XFgZA0emkpwH/A5wD0BVvV1VbwI3AXubq+0Fbp5VkJIkSW01zDTfR4ETwPeSPJXk7iTnAhdV1TGA5vzCGcYpSZLUSsMUU2cDVwF3VdWVwK/pTekNJcmuJAeSHODEiTHDnLL2NsuVJElLZphi6ihwtKqeaLYfpFdcvZZkC0BzfrzfjatqT1WtVdUamzdPI2ZJkqTWGLgAvap+keSVJJdX1fP0Glc+25x20OsEvgN4eKaRdpUjaJIktdqw3+b7E+De5pt8LwJ/SG9U64EkO4GXgVtnE6IkSVJ7DVVMVdXTwFqfH1033XAkSZKWi+1kJEmSJmAxJU1RkhuSPJ/kheZgtlKnmRPqAospaUqSnAX8KfAfgU8AX0jyicVGJS2OOaGusJiSpudq4IWqerGq3gbuo9cpQOoqc0KdYDElTc/FwCvrto82+6SuMifUCcMeGmE6Dh78Fcnzc73P2fkI8Pqig5iSVXosMPzj+ddTvt8+Xanf29EzyS5gV7P5zyQ/n3Ici7JKr6NVeixgTizKKr2OVumxwJRzYr7FFDxfVf0OsbB0khzwsbTTAh/PUeCSddtbgVfffaWq2gPsgdX62/tY2sucWAwfS3tN+/E4zSdNz5PAZUkubQ5w+3ngkQXHJC2SOaFOmPfIlLSyqupkkj8GfgycBXy3qp5ZcFjSwpgT6op5F1N75nx/s+Rjaa+FPZ6q+hHwoxFuskp/ex9Le5kTi+Fjaa+pPp5U2UhXkiRpXK6ZkiRJmsBciqllbyeQ5JIkjyc5nOSZJLc1+zcl2ZfkSHN+waJjHVaSs5I8leTRZvvSJE80j+X+ZrFo6yU5P8mDSZ5rnp/PLMPzssw5sYr5AObEopkT7bIq+QDzyYmZF1Mr0k7gJPDVqvo48Gngj5rHsBvYX1WXAfub7WVxG3B43fY3gG81j+UNYOdCohrdd4DHqupjwBX0HlOrn5cVyIlVzAcwJxbGnGilVckHmEdOVNVMT8BngB+v274DuGPW9zvjx/QwcD3wPLCl2beF3nG0Fh7fEPFvbV481wKP0juw3uvA2f2es7aegPOA/0Oz9m/d/lY/L6uWE8ueD0285sRi4zYnWnRalXxoYp1LTsxjmm+l2gkk2Q5cCTwBXFRVxwCa8wsXF9lIvg18Dfh/zfaHgTer6mSzvSzP0UeBE8D3muHou5OcS/ufl5XJiRXJBzAnFs2caJdVyQeYU07Mo5gaqp3AMkjyQeAHwJer6peLjmccSW4EjlfVwfW7+1x1GZ6js4GrgLuq6krg1yzHMPqy/r1Pswr5AOZESyzr3/s0q5ATK5YPMKecmEcxNVQ7gbZLcg69JLm3qn7Y7H4tyZbm51uA44uKbwTXAJ9L8hK9Du7X0vsUcn6SU8cdW5bn6ChwtKqeaLYfpJc0bX9elj4nVigfwJxoA3OiPVYpH2BOOTGPYmrp2wkkCXAPcLiqvrnuR48AO5rLO+jNk7daVd1RVVuraju95+InVfVF4HHgluZqy/JYfgG8kuTyZtd1wLO0/3lZ6pxYpXwAc6IlzImWWKV8gDnmxJwWgH0W+HvgH4D/vOgFaWPE/+/oDWn+LfB0c/osvXnk/cCR5nzTomMd8XH9e+DR5vJHgb8BXgD+B/AvFh3fkI/hk8CB5rn5n8AFy/C8LHNOrGo+NI/NnFhc3OZEy06rkA9N7DPPCY+ALkmSNAGPgC5JkjQBiylJkqQJWExJkiRNwGJKkiRpAhZTkiRJE7CYkiRJmoDFlCRJ0gQspiRJkibw/wHsEfyega1kmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "+ TRAINING \tEpoch: 2 \tLoss: 1.363415\n",
      "+ TRAINING \tEpoch: 3 \tLoss: 1.326473\n",
      "+ TRAINING \tEpoch: 4 \tLoss: 1.306049\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-181-ec987b20196a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;31m# Training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_samples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataloader_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_samples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'img'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_samples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'seg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mprd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model_dir = os.path.join(out_dir, 'model')\n",
    "if not os.path.exists(model_dir):\n",
    "    os.makedirs(model_dir)\n",
    "\n",
    "torch.manual_seed(rnd_seed) #fix random seed\n",
    "\n",
    "model = SimpleNet3D(num_classes).to(device)\n",
    "model.train()\n",
    "    \n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "loss_train_log = []\n",
    "loss_val_log = []\n",
    "epoch_val_log = []\n",
    "    \n",