MRI_Segmentation_Original.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MRI Segmentation Original.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FalseFeint/MertsMinions/blob/main/MRI_Segmentation_Original.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pVD1ah_NIAWn"
      },
      "source": [
        "#Mounting Drive & Git"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgCwdeWaKh9S",
        "outputId": "57785595-fad9-47cf-a560-68de3e4dc64c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd ~/../content/drive/My\\ Drive/ML\\ Final\\ Project/MertsMinions"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/My Drive/ML Final Project/MertsMinions\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14_esg46H4UT",
        "outputId": "88daa0ad-676e-4fab-ddba-85c347eb2e67",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "! git pull"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy7RP1cVBNB2"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQPefAMY-9UX"
      },
      "source": [
        "import os\n",
        "import random\n",
        "\n",
        "from collections import OrderedDict\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.backends.backend_agg import FigureCanvasAgg\n",
        "from tqdm import tqdm\n",
        "from skimage.exposure import rescale_intensity\n",
        "from skimage.io import imread, imsave\n",
        "from skimage.transform import resize, rescale, rotate\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision.transforms import Compose\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJpHMyGpBkTw"
      },
      "source": [
        "# Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Szi-SmLjBmfR"
      },
      "source": [
        "def crop_sample(x):\n",
        "    volume, mask = x\n",
        "    volume[volume < np.max(volume) * 0.1] = 0\n",
        "    z_projection = np.max(np.max(np.max(volume, axis=-1), axis=-1), axis=-1)\n",
        "    z_nonzero = np.nonzero(z_projection)\n",
        "    z_min = np.min(z_nonzero)\n",
        "    z_max = np.max(z_nonzero) + 1\n",
        "    y_projection = np.max(np.max(np.max(volume, axis=0), axis=-1), axis=-1)\n",
        "    y_nonzero = np.nonzero(y_projection)\n",
        "    y_min = np.min(y_nonzero)\n",
        "    y_max = np.max(y_nonzero) + 1\n",
        "    x_projection = np.max(np.max(np.max(volume, axis=0), axis=0), axis=-1)\n",
        "    x_nonzero = np.nonzero(x_projection)\n",
        "    x_min = np.min(x_nonzero)\n",
        "    x_max = np.max(x_nonzero) + 1\n",
        "    return (\n",
        "        volume[z_min:z_max, y_min:y_max, x_min:x_max],\n",
        "        mask[z_min:z_max, y_min:y_max, x_min:x_max],\n",
        "    )\n",
        "\n",
        "\n",
        "def pad_sample(x):\n",
        "    volume, mask = x\n",
        "    a = volume.shape[1]\n",
        "    b = volume.shape[2]\n",
        "    if a == b:\n",
        "        return volume, mask\n",
        "    diff = (max(a, b) - min(a, b)) / 2.0\n",
        "    if a > b:\n",
        "        padding = ((0, 0), (0, 0), (int(np.floor(diff)), int(np.ceil(diff))))\n",
        "    else:\n",
        "        padding = ((0, 0), (int(np.floor(diff)), int(np.ceil(diff))), (0, 0))\n",
        "    mask = np.pad(mask, padding, mode=\"constant\", constant_values=0)\n",
        "    padding = padding + ((0, 0),)\n",
        "    volume = np.pad(volume, padding, mode=\"constant\", constant_values=0)\n",
        "    return volume, mask\n",
        "\n",
        "\n",
        "def resize_sample(x, size=256):\n",
        "    volume, mask = x\n",
        "    v_shape = volume.shape\n",
        "    out_shape = (v_shape[0], size, size)\n",
        "    mask = resize(\n",
        "        mask,\n",
        "        output_shape=out_shape,\n",
        "        order=0,\n",
        "        mode=\"constant\",\n",
        "        cval=0,\n",
        "        anti_aliasing=False,\n",
        "    )\n",
        "    out_shape = out_shape + (v_shape[3],)\n",
        "    volume = resize(\n",
        "        volume,\n",
        "        output_shape=out_shape,\n",
        "        order=2,\n",
        "        mode=\"constant\",\n",
        "        cval=0,\n",
        "        anti_aliasing=False,\n",
        "    )\n",
        "    return volume, mask\n",
        "\n",
        "\n",
        "def normalize_volume(volume):\n",
        "    p10 = np.percentile(volume, 10)\n",
        "    p99 = np.percentile(volume, 99)\n",
        "    volume = rescale_intensity(volume, in_range=(p10, p99))\n",
        "    m = np.mean(volume, axis=(0, 1, 2))\n",
        "    s = np.std(volume, axis=(0, 1, 2))\n",
        "    volume = (volume - m) / s\n",
        "    return volume"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-E1xNb7TBwYn"
      },
      "source": [
        "# Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJb2acyWByrA"
      },
      "source": [
        "def transforms(scale=None, angle=None, flip_prob=None):\n",
        "    transform_list = []\n",
        "\n",
        "    if scale is not None:\n",
        "        transform_list.append(Scale(scale))\n",
        "    if angle is not None:\n",
        "        transform_list.append(Rotate(angle))\n",
        "    if flip_prob is not None:\n",
        "        transform_list.append(HorizontalFlip(flip_prob))\n",
        "\n",
        "    return Compose(transform_list)\n",
        "\n",
        "\n",
        "class Scale(object):\n",
        "\n",
        "    def __init__(self, scale):\n",
        "        self.scale = scale\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        image, mask = sample\n",
        "\n",
        "        img_size = image.shape[0]\n",
        "\n",
        "        scale = np.random.uniform(low=1.0 - self.scale, high=1.0 + self.scale)\n",
        "\n",
        "        image = rescale(\n",
        "            image,\n",
        "            (scale, scale),\n",
        "            multichannel=True,\n",
        "            preserve_range=True,\n",
        "            mode=\"constant\",\n",
        "            anti_aliasing=False,\n",
        "        )\n",
        "        mask = rescale(\n",
        "            mask,\n",
        "            (scale, scale),\n",
        "            order=0,\n",
        "            multichannel=True,\n",
        "            preserve_range=True,\n",
        "            mode=\"constant\",\n",
        "            anti_aliasing=False,\n",
        "        )\n",
        "\n",
        "        if scale < 1.0:\n",
        "            diff = (img_size - image.shape[0]) / 2.0\n",
        "            padding = ((int(np.floor(diff)), int(np.ceil(diff))),) * 2 + ((0, 0),)\n",
        "            image = np.pad(image, padding, mode=\"constant\", constant_values=0)\n",
        "            mask = np.pad(mask, padding, mode=\"constant\", constant_values=0)\n",
        "        else:\n",
        "            x_min = (image.shape[0] - img_size) // 2\n",
        "            x_max = x_min + img_size\n",
        "            image = image[x_min:x_max, x_min:x_max, ...]\n",
        "            mask = mask[x_min:x_max, x_min:x_max, ...]\n",
        "\n",
        "        return image, mask\n",
        "\n",
        "\n",
        "class Rotate(object):\n",
        "\n",
        "    def __init__(self, angle):\n",
        "        self.angle = angle\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        image, mask = sample\n",
        "\n",
        "        angle = np.random.uniform(low=-self.angle, high=self.angle)\n",
        "        image = rotate(image, angle, resize=False, preserve_range=True, mode=\"constant\")\n",
        "        mask = rotate(\n",
        "            mask, angle, resize=False, order=0, preserve_range=True, mode=\"constant\"\n",
        "        )\n",
        "        return image, mask\n",
        "\n",
        "\n",
        "class HorizontalFlip(object):\n",
        "\n",
        "    def __init__(self, flip_prob):\n",
        "        self.flip_prob = flip_prob\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        image, mask = sample\n",
        "\n",
        "        if np.random.rand() > self.flip_prob:\n",
        "            return image, mask\n",
        "\n",
        "        image = np.fliplr(image).copy()\n",
        "        mask = np.fliplr(mask).copy()\n",
        "\n",
        "        return image, mask"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuqvstzy-rPf"
      },
      "source": [
        "#UNet\n",
        "Create UNet architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLGnOJ8P_R9Y"
      },
      "source": [
        "\n",
        "class UNet(nn.Module):\n",
        "\n",
        "  def __init__(self, in_channels=3, out_channels=1, init_features=32):\n",
        "      super(UNet, self).__init__()\n",
        "\n",
        "      features = init_features\n",
        "      self.encoder1 = UNet._block(in_channels, features, name=\"enc1\")\n",
        "      self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "      self.encoder2 = UNet._block(features, features * 2, name=\"enc2\")\n",
        "      self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "      self.encoder3 = UNet._block(features * 2, features * 4, name=\"enc3\")\n",
        "      self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "      self.encoder4 = UNet._block(features * 4, features * 8, name=\"enc4\")\n",
        "      self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "\n",
        "      self.bottleneck = UNet._block(features * 8, features * 16, name=\"bottleneck\")\n",
        "\n",
        "      self.upconv4 = nn.ConvTranspose2d(\n",
        "          features * 16, features * 8, kernel_size=2, stride=2\n",
        "      )\n",
        "      self.decoder4 = UNet._block((features * 8) * 2, features * 8, name=\"dec4\")\n",
        "      self.upconv3 = nn.ConvTranspose2d(\n",
        "          features * 8, features * 4, kernel_size=2, stride=2\n",
        "      )\n",
        "      self.decoder3 = UNet._block((features * 4) * 2, features * 4, name=\"dec3\")\n",
        "      self.upconv2 = nn.ConvTranspose2d(\n",
        "          features * 4, features * 2, kernel_size=2, stride=2\n",
        "      )\n",
        "      self.decoder2 = UNet._block((features * 2) * 2, features * 2, name=\"dec2\")\n",
        "      self.upconv1 = nn.ConvTranspose2d(\n",
        "          features * 2, features, kernel_size=2, stride=2\n",
        "      )\n",
        "      self.decoder1 = UNet._block(features * 2, features, name=\"dec1\")\n",
        "\n",
        "      self.conv = nn.Conv2d(\n",
        "          in_channels=features, out_channels=out_channels, kernel_size=1\n",
        "      )\n",
        "\n",
        "  def forward(self, x):\n",
        "      enc1 = self.encoder1(x)\n",
        "      enc2 = self.encoder2(self.pool1(enc1))\n",
        "      enc3 = self.encoder3(self.pool2(enc2))\n",
        "      enc4 = self.encoder4(self.pool3(enc3))\n",
        "\n",
        "      bottleneck = self.bottleneck(self.pool4(enc4))\n",
        "\n",
        "      dec4 = self.upconv4(bottleneck)\n",
        "      dec4 = torch.cat((dec4, enc4), dim=1)\n",
        "      dec4 = self.decoder4(dec4)\n",
        "      dec3 = self.upconv3(dec4)\n",
        "      dec3 = torch.cat((dec3, enc3), dim=1)\n",
        "      dec3 = self.decoder3(dec3)\n",
        "      dec2 = self.upconv2(dec3)\n",
        "      dec2 = torch.cat((dec2, enc2), dim=1)\n",
        "      dec2 = self.decoder2(dec2)\n",
        "      dec1 = self.upconv1(dec2)\n",
        "      dec1 = torch.cat((dec1, enc1), dim=1)\n",
        "      dec1 = self.decoder1(dec1)\n",
        "      return torch.sigmoid(self.conv(dec1))\n",
        "\n",
        "  \"\"\"\" \n",
        "  This function is useful for blocking together convolutions, activations, \n",
        "  and max pooling for each \"meta-layer\" in our U-Net architecture \n",
        "\n",
        "  Convolution layers in this block have specified in channels, \n",
        "  features, a kernel size of 3, padding of 1 and no bias\n",
        "\n",
        "  We use batch normalization to reduce internal covariance\n",
        "\n",
        "  ReLU is used for non-linearity activation\n",
        "  \"\"\"\n",
        "  @staticmethod\n",
        "  def _block(in_channels, features, name):\n",
        "      return nn.Sequential(\n",
        "          OrderedDict(\n",
        "              [\n",
        "                  (\n",
        "                      name + \"conv1\",\n",
        "                      nn.Conv2d(\n",
        "                          in_channels=in_channels,\n",
        "                          out_channels=features,\n",
        "                          kernel_size=3,\n",
        "                          padding=1,\n",
        "                          bias=False,\n",
        "                      ),\n",
        "                  ),\n",
        "                  (name + \"norm1\", nn.BatchNorm2d(num_features=features)),\n",
        "                  (name + \"relu1\", nn.ReLU(inplace=True)),\n",
        "                  (\n",
        "                      name + \"conv2\",\n",
        "                      nn.Conv2d(\n",
        "                          in_channels=features,\n",
        "                          out_channels=features,\n",
        "                          kernel_size=3,\n",
        "                          padding=1,\n",
        "                          bias=False,\n",
        "                      ),\n",
        "                  ),\n",
        "                  (name + \"norm2\", nn.BatchNorm2d(num_features=features)),\n",
        "                  (name + \"relu2\", nn.ReLU(inplace=True)),\n",
        "              ]\n",
        "          )\n",
        "      )\n",
        "\n",
        "\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAPdjYmjn95Y"
      },
      "source": [
        "UNet = UNet()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d8-7-FrCRI4"
      },
      "source": [
        "# Dice Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCapGGU1CU0w"
      },
      "source": [
        "class DiceLoss(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(DiceLoss, self).__init__()\n",
        "        self.smooth = 1.0\n",
        "\n",
        "    def forward(self, y_pred, y_true):\n",
        "        assert y_pred.size() == y_true.size()\n",
        "        y_pred = y_pred[:, 0].contiguous().view(-1)\n",
        "        y_true = y_true[:, 0].contiguous().view(-1)\n",
        "        intersection = (y_pred * y_true).sum()\n",
        "        dsc = (2. * intersection + self.smooth) / (\n",
        "            y_pred.sum() + y_true.sum() + self.smooth\n",
        "        )\n",
        "        return 1. - dsc"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nk9I7WvmUfof"
      },
      "source": [
        "# Dataset Utilities"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuLK3SxgUi6z"
      },
      "source": [
        "def data_loaders(batch_size, workers, image_size, aug_scale, aug_angle):\n",
        "    dataset_train, dataset_valid = datasets(\"./dataset\", image_size, aug_scale, aug_angle)\n",
        "\n",
        "    def worker_init(worker_id):\n",
        "        np.random.seed(42 + worker_id)\n",
        "\n",
        "    loader_train = DataLoader(\n",
        "        dataset_train,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=True,\n",
        "        drop_last=True,\n",
        "        num_workers=workers,\n",
        "        worker_init_fn=worker_init,\n",
        "    )\n",
        "    loader_valid = DataLoader(\n",
        "        dataset_valid,\n",
        "        batch_size=batch_size,\n",
        "        drop_last=False,\n",
        "        num_workers=workers,\n",
        "        worker_init_fn=worker_init,\n",
        "    )\n",
        "\n",
        "    return loader_train, loader_valid\n",
        "\n",
        "\n",
        "def datasets(images, image_size, aug_scale, aug_angle):\n",
        "    train = BrainSegmentationDataset(\n",
        "        images_dir=images,\n",
        "        subset=\"train\",\n",
        "        image_size=image_size,\n",
        "        # transform=transforms(scale=aug_scale, angle=aug_angle, flip_prob=0.5),\n",
        "        transform=None,\n",
        "\n",
        "    )\n",
        "    valid = BrainSegmentationDataset(\n",
        "        images_dir=images,\n",
        "        subset=\"validation\",\n",
        "        image_size=image_size,\n",
        "        random_sampling=False,\n",
        "    )\n",
        "    return train, valid\n",
        "\n",
        "class BrainSegmentationDataset(Dataset):\n",
        "    \"\"\"Brain MRI dataset for FLAIR abnormality segmentation\"\"\"\n",
        "\n",
        "    in_channels = 3\n",
        "    out_channels = 1\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        images_dir,\n",
        "        transform=None,\n",
        "        image_size=256,\n",
        "        subset=\"train\",\n",
        "        random_sampling=True,\n",
        "        seed=42,\n",
        "    ):\n",
        "        assert subset in [\"all\", \"train\", \"validation\"]\n",
        "\n",
        "        # read images\n",
        "        volumes = {}\n",
        "        masks = {}\n",
        "        print(\"reading {} images...\".format(subset))\n",
        "        for (dirpath, dirnames, filenames) in os.walk(images_dir):\n",
        "            image_slices = []\n",
        "            mask_slices = []\n",
        "            for filename in sorted(\n",
        "                filter(lambda f: \".tif\" in f, filenames),\n",
        "                key=lambda x: int(x.split(\".\")[-2].split(\"_\")[4]),\n",
        "            ):\n",
        "                filepath = os.path.join(dirpath, filename)\n",
        "                if \"mask\" in filename:\n",
        "                    mask_slices.append(imread(filepath, as_gray=True))\n",
        "                else:\n",
        "                    image_slices.append(imread(filepath))\n",
        "            patient_id = \"None\"\n",
        "            if len(image_slices) > 0:\n",
        "                patient_id = dirpath.split(\"/\")[-1]\n",
        "                volumes[patient_id] = np.array(image_slices[1:-1])\n",
        "                masks[patient_id] = np.array(mask_slices[1:-1])\n",
        "            dictIndex = len(volumes)\n",
        "            print(\"Volume: \" + str(dictIndex) \\\n",
        "                  + \" Patient ID: \" + str(patient_id) \\\n",
        "                  + \" Images: \" + str(len(image_slices)) \\\n",
        "                  + \" Masks: \" + str(len(mask_slices)))\n",
        "\n",
        "        self.patients = sorted(volumes)\n",
        "\n",
        "        # select cases to subset\n",
        "        if not subset == \"all\":\n",
        "            random.seed(seed)\n",
        "            validation_patients = random.sample(self.patients, k=10)\n",
        "            if subset == \"validation\":\n",
        "                self.patients = validation_patients\n",
        "            else:\n",
        "                self.patients = sorted(\n",
        "                    list(set(self.patients).difference(validation_patients))\n",
        "                )\n",
        "\n",
        "        print(\"preprocessing {} volumes...\".format(subset))\n",
        "        # create list of tuples (volume, mask)\n",
        "        self.volumes = [(volumes[k], masks[k]) for k in self.patients]\n",
        "\n",
        "        print(\"cropping {} volumes...\".format(subset))\n",
        "        # crop to smallest enclosing volume\n",
        "        self.volumes = [crop_sample(v) for v in self.volumes]\n",
        "\n",
        "        print(\"padding {} volumes...\".format(subset))\n",
        "        # pad to square\n",
        "        self.volumes = [pad_sample(v) for v in self.volumes]\n",
        "\n",
        "        print(\"resizing {} volumes...\".format(subset))\n",
        "        # resize\n",
        "        self.volumes = [resize_sample(v, size=image_size) for v in self.volumes]\n",
        "\n",
        "        print(\"normalizing {} volumes...\".format(subset))\n",
        "        # normalize channel-wise\n",
        "        self.volumes = [(normalize_volume(v), m) for v, m in self.volumes]\n",
        "\n",
        "        # probabilities for sampling slices based on masks\n",
        "        self.slice_weights = [m.sum(axis=-1).sum(axis=-1) for v, m in self.volumes]\n",
        "        self.slice_weights = [\n",
        "            (s + (s.sum() * 0.1 / len(s))) / (s.sum() * 1.1) for s in self.slice_weights\n",
        "        ]\n",
        "\n",
        "        # add channel dimension to masks\n",
        "        self.volumes = [(v, m[..., np.newaxis]) for (v, m) in self.volumes]\n",
        "\n",
        "        print(\"done creating {} dataset\".format(subset))\n",
        "\n",
        "        # create global index for patient and slice (idx -> (p_idx, s_idx))\n",
        "        num_slices = [v.shape[0] for v, m in self.volumes]\n",
        "        self.patient_slice_index = list(\n",
        "            zip(\n",
        "                sum([[i] * num_slices[i] for i in range(len(num_slices))], []),\n",
        "                sum([list(range(x)) for x in num_slices], []),\n",
        "            )\n",
        "        )\n",
        "\n",
        "        self.random_sampling = random_sampling\n",
        "\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.patient_slice_index)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        patient = self.patient_slice_index[idx][0]\n",
        "        slice_n = self.patient_slice_index[idx][1]\n",
        "\n",
        "        if self.random_sampling:\n",
        "            patient = np.random.randint(len(self.volumes))\n",
        "            slice_n = np.random.choice(\n",
        "                range(self.volumes[patient][0].shape[0]), p=self.slice_weights[patient]\n",
        "            )\n",
        "\n",
        "        v, m = self.volumes[patient]\n",
        "        image = v[slice_n]\n",
        "        mask = m[slice_n]\n",
        "\n",
        "        if self.transform is not None:\n",
        "            image, mask = self.transform((image, mask))\n",
        "\n",
        "        # fix dimensions (C, H, W)\n",
        "        image = image.transpose(2, 0, 1)\n",
        "        mask = mask.transpose(2, 0, 1)\n",
        "\n",
        "        image_tensor = torch.from_numpy(image.astype(np.float32))\n",
        "        mask_tensor = torch.from_numpy(mask.astype(np.float32))\n",
        "\n",
        "        # return tensors\n",
        "        return image_tensor, mask_tensor"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HusoMDrlUWTq"
      },
      "source": [
        "# Post Processing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZPesmTjbUZ06"
      },
      "source": [
        "def log_images(x, y_true, y_pred, channel=1):\n",
        "    images = []\n",
        "    x_np = x[:, channel].cpu().numpy()\n",
        "    y_true_np = y_true[:, 0].cpu().numpy()\n",
        "    y_pred_np = y_pred[:, 0].cpu().numpy()\n",
        "    for i in range(x_np.shape[0]):\n",
        "        image = gray2rgb(np.squeeze(x_np[i]))\n",
        "        image = outline(image, y_pred_np[i], color=[255, 0, 0])\n",
        "        image = outline(image, y_true_np[i], color=[0, 255, 0])\n",
        "        images.append(image)\n",
        "    return images\n",
        "\n",
        "\n",
        "def gray2rgb(image):\n",
        "    w, h = image.shape\n",
        "    image += np.abs(np.min(image))\n",
        "    image_max = np.abs(np.max(image))\n",
        "    if image_max > 0:\n",
        "        image /= image_max\n",
        "    ret = np.empty((w, h, 3), dtype=np.uint8)\n",
        "    ret[:, :, 2] = ret[:, :, 1] = ret[:, :, 0] = image * 255\n",
        "    return ret\n",
        "\n",
        "\n",
        "def outline(image, mask, color):\n",
        "    mask = np.round(mask)\n",
        "    yy, xx = np.nonzero(mask)\n",
        "    for y, x in zip(yy, xx):\n",
        "        if 0.0 < np.mean(mask[max(0, y - 1) : y + 2, max(0, x - 1) : x + 2]) < 1.0:\n",
        "            image[max(0, y) : y + 1, max(0, x) : x + 1] = color\n",
        "    return image\n",
        "\n",
        "def dsc(y_pred, y_true):\n",
        "    y_pred = np.round(y_pred).astype(int)\n",
        "    y_true = np.round(y_true).astype(int)\n",
        "    return np.sum(y_pred[y_true == 1]) * 2.0 / (np.sum(y_pred) + np.sum(y_true))\n",
        "\n",
        "\n",
        "def dsc_distribution(volumes):\n",
        "    dsc_dict = {}\n",
        "    for p in volumes:\n",
        "        y_pred = volumes[p][1]\n",
        "        y_true = volumes[p][2]\n",
        "        dsc_dict[p] = dsc(y_pred, y_true)\n",
        "    return dsc_dict\n",
        "\n",
        "\n",
        "def dsc_per_volume(validation_pred, validation_true, patient_slice_index):\n",
        "    dsc_list = []\n",
        "    num_slices = np.bincount([p[0] for p in patient_slice_index])\n",
        "    index = 0\n",
        "    for p in range(len(num_slices)):\n",
        "        y_pred = np.array(validation_pred[index : index + num_slices[p]])\n",
        "        y_true = np.array(validation_true[index : index + num_slices[p]])\n",
        "        dsc_list.append(dsc(y_pred, y_true))\n",
        "        index += num_slices[p]\n",
        "    return dsc_list\n",
        "\n",
        "\n",
        "def postprocess_per_volume(\n",
        "    input_list, pred_list, true_list, patient_slice_index, patients\n",
        "):\n",
        "    volumes = {}\n",
        "    num_slices = np.bincount([p[0] for p in patient_slice_index])\n",
        "    index = 0\n",
        "    for p in range(len(num_slices)):\n",
        "        volume_in = np.array(input_list[index : index + num_slices[p]])\n",
        "        volume_pred = np.round(\n",
        "            np.array(pred_list[index : index + num_slices[p]])\n",
        "        ).astype(int)\n",
        "        volume_true = np.array(true_list[index : index + num_slices[p]])\n",
        "        volumes[patients[p]] = (volume_in, volume_pred, volume_true)\n",
        "        index += num_slices[p]\n",
        "    return volumes\n",
        "\n",
        "\n",
        "def log_loss_summary(loss, step, prefix=\"\"):\n",
        "    print(\"epoch {} | {}: {}\".format(step + 1, prefix + \"loss\", np.mean(loss)))\n",
        "\n",
        "def log_scalar_summary(tag, value, step):\n",
        "    print(\"epoch {} | {}: {}\".format(step + 1, tag, value))\n",
        "\n",
        "\n",
        "def plot_dsc(dsc_dist):\n",
        "    y_positions = np.arange(len(dsc_dist))\n",
        "    dsc_dist = sorted(dsc_dist.items(), key=lambda x: x[1])\n",
        "    values = [x[1] for x in dsc_dist]\n",
        "    labels = [x[0] for x in dsc_dist]\n",
        "    labels = [\"_\".join(l.split(\"_\")[1:-1]) for l in labels]\n",
        "    fig = plt.figure(figsize=(12, 8))\n",
        "    canvas = FigureCanvasAgg(fig)\n",
        "    plt.barh(y_positions, values, align=\"center\", color=\"skyblue\")\n",
        "    plt.yticks(y_positions, labels)\n",
        "    plt.xticks(np.arange(0.0, 1.0, 0.1))\n",
        "    plt.xlim([0.0, 1.0])\n",
        "    plt.gca().axvline(np.mean(values), color=\"tomato\", linewidth=2)\n",
        "    plt.gca().axvline(np.median(values), color=\"forestgreen\", linewidth=2)\n",
        "    plt.xlabel(\"Dice coefficient\", fontsize=\"x-large\")\n",
        "    plt.gca().xaxis.grid(color=\"silver\", alpha=0.5, linestyle=\"--\", linewidth=1)\n",
        "    plt.tight_layout()\n",
        "    canvas.draw()\n",
        "    plt.close()\n",
        "    s, (width, height) = canvas.print_to_buffer()\n",
        "    return np.fromstring(s, np.uint8).reshape((height, width, 4))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEz3JsOZBooz"
      },
      "source": [
        "#Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ggt4jAnGXtAn"
      },
      "source": [
        "## Parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZMIURGJWEpZ"
      },
      "source": [
        "## Create Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7wkPjZyXvP3"
      },
      "source": [
        "batch_size = 16\n",
        "epochs = 50\n",
        "lr = 0.01\n",
        "workers = 2\n",
        "weights = \"./\"\n",
        "image_size = 224\n",
        "aug_scale = 0.05\n",
        "aug_angle = 15"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bkkrTm8WGoh",
        "outputId": "048f6c8a-8b6b-4b2e-b5ac-f251815a1e57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "device = torch.device(\"cpu\" if not torch.cuda.is_available() else \"cuda:0\")\n",
        "\n",
        "loader_train, loader_valid = data_loaders(batch_size, workers, image_size, aug_scale, aug_angle)\n",
        "loaders = {\"train\": loader_train, \"valid\": loader_valid}"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "reading train images...\n",
            "Volume: 0 Patient ID: None Images: 0 Masks: 0\n",
            "Volume: 1 Patient ID: TCGA_CS_4941_19960909 Images: 23 Masks: 23\n",
            "Volume: 2 Patient ID: TCGA_CS_4942_19970222 Images: 20 Masks: 20\n",
            "Volume: 3 Patient ID: TCGA_CS_4943_20000902 Images: 20 Masks: 20\n",
            "Volume: 4 Patient ID: TCGA_CS_4944_20010208 Images: 20 Masks: 20\n",
            "Volume: 5 Patient ID: TCGA_CS_5393_19990606 Images: 20 Masks: 20\n",
            "Volume: 6 Patient ID: TCGA_CS_5395_19981004 Images: 20 Masks: 20\n",
            "Volume: 7 Patient ID: TCGA_CS_5396_20010302 Images: 24 Masks: 24\n",
            "Volume: 8 Patient ID: TCGA_CS_5397_20010315 Images: 22 Masks: 22\n",
            "Volume: 9 Patient ID: TCGA_CS_6186_20000601 Images: 25 Masks: 25\n",
            "Volume: 10 Patient ID: TCGA_CS_6188_20010812 Images: 24 Masks: 24\n",
            "Volume: 11 Patient ID: TCGA_CS_6290_20000917 Images: 20 Masks: 20\n",
            "Volume: 12 Patient ID: TCGA_CS_6665_20010817 Images: 24 Masks: 24\n",
            "Volume: 13 Patient ID: TCGA_CS_6666_20011109 Images: 26 Masks: 26\n",
            "Volume: 14 Patient ID: TCGA_CS_6667_20011105 Images: 20 Masks: 20\n",
            "Volume: 15 Patient ID: TCGA_CS_6668_20011025 Images: 28 Masks: 28\n",
            "Volume: 16 Patient ID: TCGA_CS_6669_20020102 Images: 22 Masks: 22\n",
            "Volume: 17 Patient ID: TCGA_DU_5849_19950405 Images: 38 Masks: 38\n",
            "Volume: 18 Patient ID: TCGA_DU_5851_19950428 Images: 40 Masks: 40\n",
            "Volume: 19 Patient ID: TCGA_DU_5852_19950709 Images: 36 Masks: 36\n",
            "Volume: 20 Patient ID: TCGA_DU_5853_19950823 Images: 36 Masks: 36\n",
            "Volume: 21 Patient ID: TCGA_DU_5854_19951104 Images: 36 Masks: 36\n",
            "Volume: 22 Patient ID: TCGA_DU_5855_19951217 Images: 26 Masks: 26\n",
            "Volume: 23 Patient ID: TCGA_DU_5871_19941206 Images: 36 Masks: 36\n",
            "Volume: 24 Patient ID: TCGA_DU_5872_19950223 Images: 71 Masks: 71\n",
            "Volume: 25 Patient ID: TCGA_DU_5874_19950510 Images: 38 Masks: 38\n",
            "Volume: 26 Patient ID: TCGA_DU_6399_19830416 Images: 53 Masks: 53\n",
            "Volume: 27 Patient ID: TCGA_DU_6400_19830518 Images: 57 Masks: 57\n",
            "Volume: 28 Patient ID: TCGA_DU_6401_19831001 Images: 51 Masks: 51\n",
            "Volume: 29 Patient ID: TCGA_DU_6404_19850629 Images: 53 Masks: 53\n",
            "Volume: 30 Patient ID: TCGA_DU_6405_19851005 Images: 60 Masks: 60\n",
            "Volume: 31 Patient ID: TCGA_DU_6407_19860514 Images: 58 Masks: 58\n",
            "Volume: 32 Patient ID: TCGA_DU_6408_19860521 Images: 56 Masks: 56\n",
            "Volume: 33 Patient ID: TCGA_DU_7008_19830723 Images: 52 Masks: 52\n",
            "Volume: 34 Patient ID: TCGA_DU_7010_19860307 Images: 58 Masks: 58\n",
            "Volume: 35 Patient ID: TCGA_DU_7013_19860523 Images: 49 Masks: 49\n",
            "Volume: 36 Patient ID: TCGA_DU_7014_19860618 Images: 60 Masks: 60\n",
            "Volume: 37 Patient ID: TCGA_DU_7018_19911220 Images: 36 Masks: 36\n",
            "Volume: 38 Patient ID: TCGA_DU_7019_19940908 Images: 38 Masks: 38\n",
            "Volume: 39 Patient ID: TCGA_DU_7294_19890104 Images: 34 Masks: 34\n",
            "Volume: 40 Patient ID: TCGA_DU_7298_19910324 Images: 32 Masks: 32\n",
            "Volume: 41 Patient ID: TCGA_DU_7299_19910417 Images: 36 Masks: 36\n",
            "Volume: 42 Patient ID: TCGA_DU_7300_19910814 Images: 36 Masks: 36\n",
            "Volume: 43 Patient ID: TCGA_DU_7301_19911112 Images: 35 Masks: 35\n",
            "Volume: 44 Patient ID: TCGA_DU_7302_19911203 Images: 36 Masks: 36\n",
            "Volume: 45 Patient ID: TCGA_DU_7304_19930325 Images: 36 Masks: 36\n",
            "Volume: 46 Patient ID: TCGA_DU_7306_19930512 Images: 42 Masks: 42\n",
            "Volume: 47 Patient ID: TCGA_DU_7309_19960831 Images: 40 Masks: 40\n",
            "Volume: 48 Patient ID: TCGA_DU_8162_19961029 Images: 37 Masks: 37\n",
            "Volume: 49 Patient ID: TCGA_DU_8163_19961119 Images: 37 Masks: 37\n",
            "Volume: 50 Patient ID: TCGA_DU_8164_19970111 Images: 37 Masks: 37\n",
            "Volume: 51 Patient ID: TCGA_DU_8165_19970205 Images: 36 Masks: 36\n",
            "Volume: 52 Patient ID: TCGA_DU_8166_19970322 Images: 36 Masks: 36\n",
            "Volume: 53 Patient ID: TCGA_DU_8167_19970402 Images: 37 Masks: 37\n",
            "Volume: 54 Patient ID: TCGA_DU_8168_19970503 Images: 36 Masks: 36\n",
            "Volume: 55 Patient ID: TCGA_DU_A5TP_19970614 Images: 38 Masks: 38\n",
            "Volume: 56 Patient ID: TCGA_DU_A5TR_19970726 Images: 38 Masks: 38\n",
            "Volume: 57 Patient ID: TCGA_DU_A5TS_19970726 Images: 35 Masks: 35\n",
            "Volume: 58 Patient ID: TCGA_DU_A5TT_19980318 Images: 56 Masks: 56\n",
            "Volume: 59 Patient ID: TCGA_DU_A5TU_19980312 Images: 23 Masks: 23\n",
            "Volume: 60 Patient ID: TCGA_DU_A5TW_19980228 Images: 31 Masks: 31\n",
            "Volume: 61 Patient ID: TCGA_DU_A5TY_19970709 Images: 36 Masks: 36\n",
            "Volume: 62 Patient ID: TCGA_EZ_7264_20010816 Images: 24 Masks: 24\n",
            "Volume: 63 Patient ID: TCGA_FG_5962_20000626 Images: 51 Masks: 51\n",
            "Volume: 64 Patient ID: TCGA_FG_5964_20010511 Images: 26 Masks: 26\n",
            "Volume: 65 Patient ID: TCGA_FG_6688_20020215 Images: 36 Masks: 36\n",
            "Volume: 66 Patient ID: TCGA_FG_6689_20020326 Images: 48 Masks: 48\n",
            "Volume: 67 Patient ID: TCGA_FG_6690_20020226 Images: 60 Masks: 60\n",
            "Volume: 68 Patient ID: TCGA_FG_6691_20020405 Images: 48 Masks: 48\n",
            "Volume: 69 Patient ID: TCGA_FG_6692_20020606 Images: 26 Masks: 26\n",
            "Volume: 70 Patient ID: TCGA_FG_7634_20000128 Images: 27 Masks: 27\n",
            "Volume: 71 Patient ID: TCGA_FG_7637_20000922 Images: 51 Masks: 51\n",
            "Volume: 72 Patient ID: TCGA_FG_7643_20021104 Images: 48 Masks: 48\n",
            "Volume: 73 Patient ID: TCGA_FG_8189_20030516 Images: 60 Masks: 60\n",
            "Volume: 74 Patient ID: TCGA_FG_A4MT_20020212 Images: 50 Masks: 50\n",
            "Volume: 75 Patient ID: TCGA_FG_A4MU_20030903 Images: 36 Masks: 36\n",
            "Volume: 76 Patient ID: TCGA_FG_A60K_20040224 Images: 73 Masks: 73\n",
            "Volume: 77 Patient ID: TCGA_HT_7473_19970826 Images: 32 Masks: 32\n",
            "Volume: 78 Patient ID: TCGA_HT_7475_19970918 Images: 32 Masks: 32\n",
            "Volume: 79 Patient ID: TCGA_HT_7602_19951103 Images: 20 Masks: 20\n",
            "Volume: 80 Patient ID: TCGA_HT_7605_19950916 Images: 32 Masks: 32\n",
            "Volume: 81 Patient ID: TCGA_HT_7608_19940304 Images: 28 Masks: 28\n",
            "Volume: 82 Patient ID: TCGA_HT_7616_19940813 Images: 30 Masks: 30\n",
            "Volume: 83 Patient ID: TCGA_HT_7680_19970202 Images: 21 Masks: 21\n",
            "Volume: 84 Patient ID: TCGA_HT_7684_19950816 Images: 25 Masks: 25\n",
            "Volume: 85 Patient ID: TCGA_HT_7686_19950629 Images: 20 Masks: 20\n",
            "Volume: 86 Patient ID: TCGA_HT_7690_19960312 Images: 24 Masks: 24\n",
            "Volume: 87 Patient ID: TCGA_HT_7692_19960724 Images: 20 Masks: 20\n",
            "Volume: 88 Patient ID: TCGA_HT_7693_19950520 Images: 21 Masks: 21\n",
            "Volume: 89 Patient ID: TCGA_HT_7694_19950404 Images: 21 Masks: 21\n",
            "Volume: 90 Patient ID: TCGA_HT_7855_19951020 Images: 21 Masks: 21\n",
            "Volume: 91 Patient ID: TCGA_HT_7856_19950831 Images: 32 Masks: 32\n",
            "Volume: 92 Patient ID: TCGA_HT_7860_19960513 Images: 22 Masks: 22\n",
            "Volume: 93 Patient ID: TCGA_HT_7874_19950902 Images: 20 Masks: 20\n",
            "Volume: 94 Patient ID: TCGA_HT_7877_19980917 Images: 30 Masks: 30\n",
            "Volume: 95 Patient ID: TCGA_HT_7879_19981009 Images: 28 Masks: 28\n",
            "Volume: 96 Patient ID: TCGA_HT_7881_19981015 Images: 80 Masks: 80\n",
            "Volume: 97 Patient ID: TCGA_HT_7882_19970125 Images: 32 Masks: 32\n",
            "Volume: 98 Patient ID: TCGA_HT_7884_19980913 Images: 20 Masks: 20\n",
            "Volume: 99 Patient ID: TCGA_HT_8018_19970411 Images: 20 Masks: 20\n",
            "Volume: 100 Patient ID: TCGA_HT_8105_19980826 Images: 32 Masks: 32\n",
            "Volume: 101 Patient ID: TCGA_HT_8106_19970727 Images: 22 Masks: 22\n",
            "Volume: 102 Patient ID: TCGA_HT_8107_19980708 Images: 20 Masks: 20\n",
            "Volume: 103 Patient ID: TCGA_HT_8111_19980330 Images: 22 Masks: 22\n",
            "Volume: 104 Patient ID: TCGA_HT_8113_19930809 Images: 21 Masks: 21\n",
            "Volume: 105 Patient ID: TCGA_HT_8114_19981030 Images: 22 Masks: 22\n",
            "Volume: 106 Patient ID: TCGA_HT_8563_19981209 Images: 23 Masks: 23\n",
            "Volume: 107 Patient ID: TCGA_HT_A5RC_19990831 Images: 32 Masks: 32\n",
            "Volume: 108 Patient ID: TCGA_HT_A616_19991226 Images: 28 Masks: 28\n",
            "Volume: 109 Patient ID: TCGA_HT_A61A_20000127 Images: 88 Masks: 88\n",
            "Volume: 110 Patient ID: TCGA_HT_A61B_19991127 Images: 88 Masks: 88\n",
            "preprocessing train volumes...\n",
            "cropping train volumes...\n",
            "padding train volumes...\n",
            "resizing train volumes...\n",
            "normalizing train volumes...\n",
            "done creating train dataset\n",
            "reading validation images...\n",
            "Volume: 0 Patient ID: None Images: 0 Masks: 0\n",
            "Volume: 1 Patient ID: TCGA_CS_4941_19960909 Images: 23 Masks: 23\n",
            "Volume: 2 Patient ID: TCGA_CS_4942_19970222 Images: 20 Masks: 20\n",
            "Volume: 3 Patient ID: TCGA_CS_4943_20000902 Images: 20 Masks: 20\n",
            "Volume: 4 Patient ID: TCGA_CS_4944_20010208 Images: 20 Masks: 20\n",
            "Volume: 5 Patient ID: TCGA_CS_5393_19990606 Images: 20 Masks: 20\n",
            "Volume: 6 Patient ID: TCGA_CS_5395_19981004 Images: 20 Masks: 20\n",
            "Volume: 7 Patient ID: TCGA_CS_5396_20010302 Images: 24 Masks: 24\n",
            "Volume: 8 Patient ID: TCGA_CS_5397_20010315 Images: 22 Masks: 22\n",
            "Volume: 9 Patient ID: TCGA_CS_6186_20000601 Images: 25 Masks: 25\n",
            "Volume: 10 Patient ID: TCGA_CS_6188_20010812 Images: 24 Masks: 24\n",
            "Volume: 11 Patient ID: TCGA_CS_6290_20000917 Images: 20 Masks: 20\n",
            "Volume: 12 Patient ID: TCGA_CS_6665_20010817 Images: 24 Masks: 24\n",
            "Volume: 13 Patient ID: TCGA_CS_6666_20011109 Images: 26 Masks: 26\n",
            "Volume: 14 Patient ID: TCGA_CS_6667_20011105 Images: 20 Masks: 20\n",
            "Volume: 15 Patient ID: TCGA_CS_6668_20011025 Images: 28 Masks: 28\n",
            "Volume: 16 Patient ID: TCGA_CS_6669_20020102 Images: 22 Masks: 22\n",
            "Volume: 17 Patient ID: TCGA_DU_5849_19950405 Images: 38 Masks: 38\n",
            "Volume: 18 Patient ID: TCGA_DU_5851_19950428 Images: 40 Masks: 40\n",
            "Volume: 19 Patient ID: TCGA_DU_5852_19950709 Images: 36 Masks: 36\n",
            "Volume: 20 Patient ID: TCGA_DU_5853_19950823 Images: 36 Masks: 36\n",
            "Volume: 21 Patient ID: TCGA_DU_5854_19951104 Images: 36 Masks: 36\n",
            "Volume: 22 Patient ID: TCGA_DU_5855_19951217 Images: 26 Masks: 26\n",
            "Volume: 23 Patient ID: TCGA_DU_5871_19941206 Images: 36 Masks: 36\n",
            "Volume: 24 Patient ID: TCGA_DU_5872_19950223 Images: 71 Masks: 71\n",
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            "Volume: 110 Patient ID: TCGA_HT_A61B_19991127 Images: 88 Masks: 88\n",
            "preprocessing validation volumes...\n",
            "cropping validation volumes...\n",
            "padding validation volumes...\n",
            "resizing validation volumes...\n",
            "normalizing validation volumes...\n",
            "done creating validation dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKgaho64WNFT"
      },
      "source": [
        "## Create UNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mh47EFVmWQWL",
        "outputId": "c0e96b4b-611a-48f6-8596-a88bc0de7440",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "unet = UNet(in_channels=BrainSegmentationDataset.in_channels, out_channels=BrainSegmentationDataset.out_channels)\n",
        "unet.to(device)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UNet(\n",
              "  (encoder1): Sequential(\n",
              "    (enc1conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc1norm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc1relu1): ReLU(inplace=True)\n",
              "    (enc1conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc1norm2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc1relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (encoder2): Sequential(\n",
              "    (enc2conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc2norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc2relu1): ReLU(inplace=True)\n",
              "    (enc2conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc2norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc2relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (encoder3): Sequential(\n",
              "    (enc3conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc3norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc3relu1): ReLU(inplace=True)\n",
              "    (enc3conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc3norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc3relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (encoder4): Sequential(\n",
              "    (enc4conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc4norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc4relu1): ReLU(inplace=True)\n",
              "    (enc4conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (enc4norm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (enc4relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (pool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  (bottleneck): Sequential(\n",
              "    (bottleneckconv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bottlenecknorm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (bottleneckrelu1): ReLU(inplace=True)\n",
              "    (bottleneckconv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bottlenecknorm2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (bottleneckrelu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (upconv4): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2))\n",
              "  (decoder4): Sequential(\n",
              "    (dec4conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec4norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec4relu1): ReLU(inplace=True)\n",
              "    (dec4conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec4norm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec4relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (upconv3): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2))\n",
              "  (decoder3): Sequential(\n",
              "    (dec3conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec3norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec3relu1): ReLU(inplace=True)\n",
              "    (dec3conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec3norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec3relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (upconv2): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))\n",
              "  (decoder2): Sequential(\n",
              "    (dec2conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec2norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec2relu1): ReLU(inplace=True)\n",
              "    (dec2conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec2norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec2relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (upconv1): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2))\n",
              "  (decoder1): Sequential(\n",
              "    (dec1conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec1norm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec1relu1): ReLU(inplace=True)\n",
              "    (dec1conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (dec1norm2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (dec1relu2): ReLU(inplace=True)\n",
              "  )\n",
              "  (conv): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tqs3caU5WRae"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkUja6K0_6x6",
        "outputId": "fd2b8829-b790-46cd-f621-77da077e2393",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "\n",
        "# TODO: Use basic loss function in original UNet\n",
        "dsc_loss = DiceLoss()\n",
        "# dsc_loss = nn.CrossEntropyLoss();\n",
        "best_validation_dsc = 0.0\n",
        "\n",
        "# TODO: Use basic optimizer in original UNet\n",
        "# optimizer = optim.Adam(unet.parameters(), lr=lr)\n",
        "optimizer = optim.SGD(unet.parameters(), lr=lr, momentum=0.99)\n",
        "\n",
        "loss_train = []\n",
        "loss_valid = []\n",
        "\n",
        "step = 0\n",
        "\n",
        "# Keep track of average losses, training accuracy and validation accuracy for each epoch\n",
        "train_loss_history = np.zeros(epochs)\n",
        "val_loss_history   = np.zeros(epochs)\n",
        "val_dsc_history    = np.zeros(epochs)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    for phase in [\"train\", \"valid\"]:\n",
        "        if phase == \"train\":\n",
        "            unet.train()\n",
        "        else:\n",
        "            unet.eval()\n",
        "\n",
        "        validation_pred = []\n",
        "        validation_true = []\n",
        "\n",
        "        for i, data in enumerate(loaders[phase]):\n",
        "            if phase == \"train\":\n",
        "                step += 1\n",
        "\n",
        "            x, y_true = data\n",
        "            x, y_true = x.to(device), y_true.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            with torch.set_grad_enabled(phase == \"train\"):\n",
        "                y_pred = unet(x)\n",
        "\n",
        "                loss = dsc_loss(y_pred, y_true)\n",
        "\n",
        "                if phase == \"valid\":\n",
        "                    loss_valid.append(loss.item())\n",
        "                    y_pred_np = y_pred.detach().cpu().numpy()\n",
        "                    validation_pred.extend(\n",
        "                        [y_pred_np[s] for s in range(y_pred_np.shape[0])]\n",
        "                    )\n",
        "                    y_true_np = y_true.detach().cpu().numpy()\n",
        "                    validation_true.extend(\n",
        "                        [y_true_np[s] for s in range(y_true_np.shape[0])]\n",
        "                    )\n",
        "                    \n",
        "                if phase == \"train\":\n",
        "                    loss_train.append(loss.item())\n",
        "                    loss.backward()\n",
        "                    optimizer.step()\n",
        "\n",
        "        if phase == \"train\":\n",
        "            log_loss_summary(loss_train, epoch)\n",
        "            train_loss_history[epoch] = np.mean(loss_train)\n",
        "            loss_train = []\n",
        "\n",
        "        if phase == \"valid\":\n",
        "            log_loss_summary(loss_valid, epoch, prefix=\"val_\")\n",
        "            val_loss_history[epoch]  = np.mean(loss_valid)\n",
        "            mean_dsc = np.mean(\n",
        "                dsc_per_volume(\n",
        "                    validation_pred,\n",
        "                    validation_true,\n",
        "                    loader_valid.dataset.patient_slice_index,\n",
        "                )\n",
        "            )\n",
        "            val_dsc_history[epoch] = mean_dsc\n",
        "            log_scalar_summary(\"val_dsc\", mean_dsc, epoch)\n",
        "            if mean_dsc > best_validation_dsc:\n",
        "                best_validation_dsc = mean_dsc\n",
        "                torch.save(unet.state_dict(), os.path.join(weights, \"unet.pt\"))\n",
        "            loss_valid = []\n",
        "\n",
        "print(\"\\nBest validation mean DSC: {:4f}\\n\".format(best_validation_dsc))\n",
        "\n",
        "state_dict = torch.load(os.path.join(weights, \"unet.pt\"))\n",
        "unet.load_state_dict(state_dict)\n",
        "unet.eval()\n",
        "\n",
        "input_list = []\n",
        "pred_list = []\n",
        "true_list = []\n",
        "\n",
        "for i, data in enumerate(loader_valid):\n",
        "    x, y_true = data\n",
        "    x, y_true = x.to(device), y_true.to(device)\n",
        "    with torch.set_grad_enabled(False):\n",
        "        y_pred = unet(x)\n",
        "        y_pred_np = y_pred.detach().cpu().numpy()\n",
        "        pred_list.extend([y_pred_np[s] for s in range(y_pred_np.shape[0])])\n",
        "        y_true_np = y_true.detach().cpu().numpy()\n",
        "        true_list.extend([y_true_np[s] for s in range(y_true_np.shape[0])])\n",
        "        x_np = x.detach().cpu().numpy()\n",
        "        input_list.extend([x_np[s] for s in range(x_np.shape[0])])\n",
        "        \n",
        "volumes = postprocess_per_volume(\n",
        "    input_list,\n",
        "    pred_list,\n",
        "    true_list,\n",
        "    loader_valid.dataset.patient_slice_index,\n",
        "    loader_valid.dataset.patients,\n",
        ")\n",
        "\n",
        "dsc_dist = dsc_distribution(volumes)\n",
        "\n",
        "dsc_dist_plot = plot_dsc(dsc_dist)\n",
        "imsave(\"./dsc.png\", dsc_dist_plot)\n",
        "\n",
        "for p in volumes:\n",
        "    x = volumes[p][0]\n",
        "    y_pred = volumes[p][1]\n",
        "    y_true = volumes[p][2]\n",
        "    for s in range(x.shape[0]):\n",
        "        image = gray2rgb(x[s, 1])  # channel 1 is for FLAIR\n",
        "        image = outline(image, y_pred[s, 0], color=[255, 0, 0])\n",
        "        image = outline(image, y_true[s, 0], color=[0, 255, 0])\n",
        "        filename = \"{}-{}.png\".format(p, str(s).zfill(2))\n",
        "        filepath = os.path.join(\"./\", filename)\n",
        "        imsave(filepath, image)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1 | loss: 0.24489611645157522\n",
            "epoch 1 | val_loss: 0.46301086743672687\n",
            "epoch 1 | val_dsc: 0.6950631535296002\n",
            "epoch 2 | loss: 0.2123926984002957\n",
            "epoch 2 | val_loss: 0.4927329279127575\n",
            "epoch 2 | val_dsc: 0.6513960318341911\n",
            "epoch 3 | loss: 0.1771925722177212\n",
            "epoch 3 | val_loss: 0.41406924383980886\n",
            "epoch 3 | val_dsc: 0.7338804736867633\n",
            "epoch 4 | loss: 0.15504412353038788\n",
            "epoch 4 | val_loss: 0.3846552599044073\n",
            "epoch 4 | val_dsc: 0.7384072622949732\n",
            "epoch 5 | loss: 0.14036754833964202\n",
            "epoch 5 | val_loss: 0.40198575315021334\n",
            "epoch 5 | val_dsc: 0.7397325070863368\n",
            "epoch 6 | loss: 0.12938796585569015\n",
            "epoch 6 | val_loss: 0.40215444280987694\n",
            "epoch 6 | val_dsc: 0.7583709312171146\n",
            "epoch 7 | loss: 0.11948851066140029\n",
            "epoch 7 | val_loss: 0.40794528949828374\n",
            "epoch 7 | val_dsc: 0.7731642868342744\n",
            "epoch 8 | loss: 0.1183208439212579\n",
            "epoch 8 | val_loss: 0.37453071560178486\n",
            "epoch 8 | val_dsc: 0.7856003695406268\n",
            "epoch 9 | loss: 0.10757003638606805\n",
            "epoch 9 | val_loss: 0.3224774230094183\n",
            "epoch 9 | val_dsc: 0.8301138120653881\n",
            "epoch 10 | loss: 0.10061499218528087\n",
            "epoch 10 | val_loss: 0.36977375121343703\n",
            "epoch 10 | val_dsc: 0.7915783604337547\n",
            "epoch 11 | loss: 0.09518238328970395\n",
            "epoch 11 | val_loss: 0.36129224868047805\n",
            "epoch 11 | val_dsc: 0.8200209887823047\n",
            "epoch 12 | loss: 0.09016889849534401\n",
            "epoch 12 | val_loss: 0.31342933291480657\n",
            "epoch 12 | val_dsc: 0.8225969579254817\n",
            "epoch 13 | loss: 0.08433995137994106\n",
            "epoch 13 | val_loss: 0.3417985240618388\n",
            "epoch 13 | val_dsc: 0.8303557837190889\n",
            "epoch 14 | loss: 0.08148876405679263\n",
            "epoch 14 | val_loss: 0.35277223870867774\n",
            "epoch 14 | val_dsc: 0.823503552847192\n",
            "epoch 15 | loss: 0.08029806040800534\n",
            "epoch 15 | val_loss: 0.3612690397671291\n",
            "epoch 15 | val_dsc: 0.8070366579797229\n",
            "epoch 16 | loss: 0.07846212616333595\n",
            "epoch 16 | val_loss: 0.3620320217950003\n",
            "epoch 16 | val_dsc: 0.8208145677126145\n",
            "epoch 17 | loss: 0.07659530926209229\n",
            "epoch 17 | val_loss: 0.2701941558292934\n",
            "epoch 17 | val_dsc: 0.8434765473019663\n",
            "epoch 18 | loss: 0.07469698184957871\n",
            "epoch 18 | val_loss: 0.34395934570403325\n",
            "epoch 18 | val_dsc: 0.8395728688918227\n",
            "epoch 19 | loss: 0.07333124572267899\n",
            "epoch 19 | val_loss: 0.34422047649111065\n",
            "epoch 19 | val_dsc: 0.8203184284067913\n",
            "epoch 20 | loss: 0.07155064206856948\n",
            "epoch 20 | val_loss: 0.34927061909721013\n",
            "epoch 20 | val_dsc: 0.8355067859182244\n",
            "epoch 21 | loss: 0.0682112265091676\n",
            "epoch 21 | val_loss: 0.3467047924087161\n",
            "epoch 21 | val_dsc: 0.8324762511427053\n",
            "epoch 22 | loss: 0.06580436143737572\n",
            "epoch 22 | val_loss: 0.3319597670010158\n",
            "epoch 22 | val_dsc: 0.8238780211307624\n",
            "epoch 23 | loss: 0.06365352897689892\n",
            "epoch 23 | val_loss: 0.3472171255520412\n",
            "epoch 23 | val_dsc: 0.827040617166215\n",
            "epoch 24 | loss: 0.06168397630636509\n",
            "epoch 24 | val_loss: 0.350249571459634\n",
            "epoch 24 | val_dsc: 0.8281648486506521\n",
            "epoch 25 | loss: 0.06008996212711701\n",
            "epoch 25 | val_loss: 0.30754196359997704\n",
            "epoch 25 | val_dsc: 0.826364697372013\n",
            "epoch 26 | loss: 0.05779268191410945\n",
            "epoch 26 | val_loss: 0.2961356072198777\n",
            "epoch 26 | val_dsc: 0.8370983337166846\n",
            "epoch 27 | loss: 0.05583666465603388\n",
            "epoch 27 | val_loss: 0.3496155880746387\n",
            "epoch 27 | val_dsc: 0.8224457012967903\n",
            "epoch 28 | loss: 0.05502222621670136\n",
            "epoch 28 | val_loss: 0.35755379710878643\n",
            "epoch 28 | val_dsc: 0.8228695242818149\n",
            "epoch 29 | loss: 0.053597416155613385\n",
            "epoch 29 | val_loss: 0.3117023848351978\n",
            "epoch 29 | val_dsc: 0.8274617493126243\n",
            "epoch 30 | loss: 0.05229675769805908\n",
            "epoch 30 | val_loss: 0.27148817550568355\n",
            "epoch 30 | val_dsc: 0.8283325232812796\n",
            "epoch 31 | loss: 0.051193689211056784\n",
            "epoch 31 | val_loss: 0.3297789011682783\n",
            "epoch 31 | val_dsc: 0.8295702943810529\n",
            "epoch 32 | loss: 0.0501035638153553\n",
            "epoch 32 | val_loss: 0.29699926433109103\n",
            "epoch 32 | val_dsc: 0.8387306688689842\n",
            "epoch 33 | loss: 0.05060655623674393\n",
            "epoch 33 | val_loss: 0.35667487553187777\n",
            "epoch 33 | val_dsc: 0.8042134665302697\n",
            "epoch 34 | loss: 0.04956535335916739\n",
            "epoch 34 | val_loss: 0.3166759723708743\n",
            "epoch 34 | val_dsc: 0.8104790321490132\n",
            "epoch 35 | loss: 0.04622844434701479\n",
            "epoch 35 | val_loss: 0.30345577001571655\n",
            "epoch 35 | val_dsc: 0.8214241113288121\n",
            "epoch 36 | loss: 0.04408630499472985\n",
            "epoch 36 | val_loss: 0.3408517355010623\n",
            "epoch 36 | val_dsc: 0.8289926084712175\n",
            "epoch 37 | loss: 0.04273224937228056\n",
            "epoch 37 | val_loss: 0.3049109663282122\n",
            "epoch 37 | val_dsc: 0.8226779469288319\n",
            "epoch 38 | loss: 0.04110824832549462\n",
            "epoch 38 | val_loss: 0.30254463922409786\n",
            "epoch 38 | val_dsc: 0.8252411737871362\n",
            "epoch 39 | loss: 0.04041183768556668\n",
            "epoch 39 | val_loss: 0.3210310623759315\n",
            "epoch 39 | val_dsc: 0.8111569507773375\n",
            "epoch 40 | loss: 0.0397496114556606\n",
            "epoch 40 | val_loss: 0.35728821867988225\n",
            "epoch 40 | val_dsc: 0.8135597640988241\n",
            "epoch 41 | loss: 0.04003922469340838\n",
            "epoch 41 | val_loss: 0.30219554333459764\n",
            "epoch 41 | val_dsc: 0.8235567117935959\n",
            "epoch 42 | loss: 0.03984418654671082\n",
            "epoch 42 | val_loss: 0.30487305777413504\n",
            "epoch 42 | val_dsc: 0.8261521466603968\n",
            "epoch 43 | loss: 0.039268412842200354\n",
            "epoch 43 | val_loss: 0.30443680854070754\n",
            "epoch 43 | val_dsc: 0.825404382354128\n",
            "epoch 44 | loss: 0.039557232306553766\n",
            "epoch 44 | val_loss: 0.3534074908211118\n",
            "epoch 44 | val_dsc: 0.8245027013520225\n",
            "epoch 45 | loss: 0.03952031725874314\n",
            "epoch 45 | val_loss: 0.3453556015377953\n",
            "epoch 45 | val_dsc: 0.8427182289721411\n",
            "epoch 46 | loss: 0.03728619962930679\n",
            "epoch 46 | val_loss: 0.32465475513821557\n",
            "epoch 46 | val_dsc: 0.8296292564192737\n",
            "epoch 47 | loss: 0.03626251736512551\n",
            "epoch 47 | val_loss: 0.29861341203962055\n",
            "epoch 47 | val_dsc: 0.8318412383849478\n",
            "epoch 48 | loss: 0.03460889768141966\n",
            "epoch 48 | val_loss: 0.31273924169086276\n",
            "epoch 48 | val_dsc: 0.8227415325733844\n",
            "epoch 49 | loss: 0.03326928988099098\n",
            "epoch 49 | val_loss: 0.3573826125689915\n",
            "epoch 49 | val_dsc: 0.820454278116028\n",
            "epoch 50 | loss: 0.03295493527100636\n",
            "epoch 50 | val_loss: 0.3083351084164211\n",
            "epoch 50 | val_dsc: 0.8286511681472902\n",
            "\n",
            "Best validation mean DSC: 0.843477\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:104: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: ./TCGA_DU_7014_19860618-57.png is a low contrast image\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: ./TCGA_DU_6408_19860521-51.png is a low contrast image\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: ./TCGA_DU_6408_19860521-52.png is a low contrast image\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: ./TCGA_DU_6408_19860521-53.png is a low contrast image\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: ./TCGA_DU_6404_19850629-50.png is a low contrast image\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:129: UserWarning: ./TCGA_DU_5851_19950428-34.png is a low contrast image\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrOD-2kULRfM",
        "outputId": "75d0da17-6d53-4526-cefb-fb53371e31f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(val_loss_history)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.46301087 0.49273293 0.41406924 0.38465526 0.40198575 0.40215444\n",
            " 0.40794529 0.37453072 0.32247742 0.36977375 0.36129225 0.31342933\n",
            " 0.34179852 0.35277224 0.36126904 0.36203202 0.27019416 0.34395935\n",
            " 0.34422048 0.34927062 0.34670479 0.33195977 0.34721713 0.35024957\n",
            " 0.30754196 0.29613561 0.34961559 0.3575538  0.31170238 0.27148818\n",
            " 0.3297789  0.29699926 0.35667488 0.31667597 0.30345577 0.34085174\n",
            " 0.30491097 0.30254464 0.32103106 0.35728822 0.30219554 0.30487306\n",
            " 0.30443681 0.35340749 0.3453556  0.32465476 0.29861341 0.31273924\n",
            " 0.35738261 0.30833511]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Cbd3VXgB7cn"
      },
      "source": [
        "# Plot Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAUSHv39B9lB",
        "outputId": "8b53d15c-b0b6-4d39-f6c4-518797e74a5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "source": [
        "# Plot loss\n",
        "fig, axs = plt.subplots(1, 2, figsize=(11,5))\n",
        "\n",
        "ax = axs[0]\n",
        "ax.set_title(\"Dice Loss\", fontsize=20)\n",
        "ax.plot(train_loss_history, label=\"Training\")\n",
        "ax.plot(val_loss_history,   label=\"Validation\")\n",
        "ax.set_xlabel(\"Epoch\",        fontsize=16)\n",
        "ax.set_ylabel(\"Dice Loss\", fontsize=16)\n",
        "ax.legend(loc=\"best\",         fontsize=16)\n",
        "\n",
        "ax = axs[1]\n",
        "ax.set_title(\"Mean DSC Accuracy\", fontsize=22)\n",
        "ax.plot(val_dsc_history,   label=\"Validation\")\n",
        "ax.set_xlabel(\"Epoch\",        fontsize=16)\n",
        "ax.set_ylabel(\"Mean DSC\",     fontsize=16)\n",
        "ax.legend(loc=\"best\",         fontsize=16)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe0e5754da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}