Spleen2021.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Spleen2021.ipynb",
      "provenance": [],
      "mount_file_id": "1YQ-fmcL-ufnlR02T3Ter44B2AqH2QUzU",
      "authorship_tag": "ABX9TyOkeGt5NaOmluNoM9pF2ktk",
      "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/Meddebma/pyradiomics/blob/master/Spleen2021.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc4ciYTYcbqm"
      },
      "source": [
        "**Setup environment**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2zJLVABdPoc",
        "outputId": "2bb83526-dd61-42e1-b3b4-d2cf9525b56a"
      },
      "source": [
        "%pip install monai-weekly\n",
        "%pip install 'monai[all]'\n",
        "%pip install matplotlib"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQIlAusRdgBU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "fc195e6c-102a-47c9-e7f5-adee2b61e7d8"
      },
      "source": [
        "import logging\n",
        "import os\n",
        "import sys\n",
        "import tempfile\n",
        "from glob import glob\n",
        "\n",
        "import nibabel as nib\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "\n",
        "import monai\n",
        "from monai import config\n",
        "from monai.data import ITKReader, NiftiSaver, DataLoader, Dataset, ImageReader, ArrayDataset\n",
        "from monai.inferers import sliding_window_inference\n",
        "from monai.metrics import DiceMetric\n",
        "from monai.networks.nets import UNet\n",
        "from monai.transforms import Activations, AddChannel, AsDiscrete, Compose, RandRotate90, RandSpatialCrop, ScaleIntensity, ToTensor\n",
        "from monai.visualize import plot_2d_or_3d_image\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-cc278137ea86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mITKReader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNiftiSaver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mImageReader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mArrayDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/monai/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;31m# load all modules, this will trigger all export decorations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mload_submodules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude_pattern\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexcludes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/monai/utils/module.py\u001b[0m in \u001b[0;36mload_submodules\u001b[0;34m(basemod, load_all, exclude_pattern)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_pkg\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mload_all\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude_pattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m                 \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m                 \u001b[0mimporter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m                 \u001b[0msubmodules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/monai/data/image_dataset.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDtypeLike\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_reader\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImageReader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLoadImage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRandomizable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'DtypeLike' from 'monai.config' (/usr/local/lib/python3.7/dist-packages/monai/config/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "J3jdRafHIeXs",
        "outputId": "b89066b4-e8b1-4a39-cf21-379f7b277775"
      },
      "source": [
        "from monai.data import ImageDataset"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-755d0a94ba24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImageDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'ImageDataset' from 'monai.data' (/usr/local/lib/python3.7/dist-packages/monai/data/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJixURgm4NCC"
      },
      "source": [
        "**Load Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePSVcjzq4dwm",
        "outputId": "982ac3c2-83b6-491f-b2dd-f6ee879256cb"
      },
      "source": [
        "root= \"/content/drive/My Drive/Task09_Spleen/\"\n",
        "images = sorted(glob(os.path.join(root, \"imagesTr\",\"*.nii.gz\")))\n",
        "segs = sorted(glob(os.path.join(root, \"labelsTr\", \"*.nii.gz\")))\n",
        "\n",
        "val_files = [{\"img\": img, \"seg\": seg} for img, seg in zip(images, segs)]\n",
        "print (len(images))\n",
        "print (len(segs))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41\n",
            "41\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4y6vcnD86EC8"
      },
      "source": [
        "**Setup transforms for training and validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWhyBx6M6F9g"
      },
      "source": [
        "imtrans = Compose([ScaleIntensity(), AddChannel(), ToTensor()])\n",
        "segtrans = Compose([AddChannel(), ToTensor()])\n",
        "val_ds = ArrayDataset(images, imtrans, segs, segtrans)\n",
        "    # sliding window inference for one image at every iteration\n",
        "val_loader = DataLoader(val_ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available())\n",
        "dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n",
        "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])\n",
        "\n",
        "\n",
        "# define image dataset, data loader\n",
        "    check_ds = ImageDataset(images, segs, transform=train_imtrans, seg_transform=train_segtrans)\n",
        "    check_loader = DataLoader(check_ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())\n",
        "    im, seg = monai.utils.misc.first(check_loader)\n",
        "    print(im.shape, seg.shape)\n",
        "\n",
        "    # create a training data loader\n",
        "    train_ds = ImageDataset(images[:20], segs[:20], transform=train_imtrans, seg_transform=train_segtrans)\n",
        "    train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available())\n",
        "    # create a validation data loader\n",
        "    val_ds = ImageDataset(images[-20:], segs[-20:], transform=val_imtrans, seg_transform=val_segtrans)\n",
        "    val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "    dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n",
        "    post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MKBuCTFGLRM"
      },
      "source": [
        "train_imtrans = Compose(\n",
        "        [\n",
        "            ScaleIntensity(),\n",
        "            AddChannel(),\n",
        "            RandSpatialCrop((96, 96, 96), random_size=False),\n",
        "            RandRotate90(prob=0.5, spatial_axes=(0, 2)),\n",
        "            ToTensor(),\n",
        "        ]\n",
        "    )\n",
        "train_segtrans = Compose(\n",
        "        [\n",
        "            AddChannel(),\n",
        "            RandSpatialCrop((96, 96, 96), random_size=False),\n",
        "            RandRotate90(prob=0.5, spatial_axes=(0, 2)),\n",
        "            ToTensor(),\n",
        "        ]\n",
        "    )\n",
        "val_imtrans = Compose([ScaleIntensity(), AddChannel(), ToTensor()])\n",
        "val_segtrans = Compose([AddChannel(), ToTensor()])\n",
        "\n",
        "# define image dataset, data loader\n",
        "check_ds = ArrayDataset(images, train_imtrans, segs, train_segtrans)\n",
        "check_loader = DataLoader(check_ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())\n",
        "#im, seg = monai.utils.misc.first(check_loader)\n",
        "#print(im.shape, seg.shape)\n",
        "\n",
        "# create a training data loader\n",
        "train_ds = ArrayDataset(images[:20], train_imtrans, segs[:20], train_segtrans)\n",
        "train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available())\n",
        "# create a validation data loader\n",
        "val_ds = ArrayDataset(images[-20:], val_imtrans, segs[-20:], val_segtrans)\n",
        "val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, pin_memory=torch.cuda.is_available())\n",
        "dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n",
        "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYpMmc8LEQR1"
      },
      "source": [
        "**Model UNET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWaapDvHELgG"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = UNet(\n",
        "        dimensions=3,\n",
        "        in_channels=1,\n",
        "        out_channels=1,\n",
        "        channels=(16, 32, 64, 128, 256),\n",
        "        strides=(2, 2, 2, 2),\n",
        "        num_res_units=2,\n",
        "    ).to(device)\n",
        "loss_function = monai.losses.DiceLoss(sigmoid=True)\n",
        "optimizer = torch.optim.Adam(model.parameters(), 1e-3)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ky3dxbmdEbIc"
      },
      "source": [
        "**Pytorch Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "v8j4agOqEj3c",
        "outputId": "2a11eeea-da3f-4bfb-9613-05ffa0091ade"
      },
      "source": [
        "val_interval = 2\n",
        "best_metric = -1\n",
        "best_metric_epoch = -1\n",
        "epoch_loss_values = list()\n",
        "metric_values = list()\n",
        "writer = SummaryWriter()\n",
        "for epoch in range(5):\n",
        "        print(\"-\" * 10)\n",
        "        print(f\"epoch {epoch + 1}/{5}\")\n",
        "        model.train()\n",
        "        epoch_loss = 0\n",
        "        step = 0\n",
        "        for batch_data in train_loader:\n",
        "            step += 1\n",
        "            inputs, labels = batch_data[0].to(device), batch_data[1].to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = loss_function(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            epoch_loss += loss.item()\n",
        "            epoch_len = len(train_ds) // train_loader.batch_size\n",
        "            print(f\"{step}/{epoch_len}, train_loss: {loss.item():.4f}\")\n",
        "            writer.add_scalar(\"train_loss\", loss.item(), epoch_len * epoch + step)\n",
        "        epoch_loss /= step\n",
        "        epoch_loss_values.append(epoch_loss)\n",
        "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "        if (epoch + 1) % val_interval == 0:\n",
        "            model.eval()\n",
        "            with torch.no_grad():\n",
        "                metric_sum = 0.0\n",
        "                metric_count = 0\n",
        "                val_images = None\n",
        "                val_labels = None\n",
        "                val_outputs = None\n",
        "                for val_data in val_loader:\n",
        "                    val_images, val_labels = val_data[0].to(device), val_data[1].to(device)\n",
        "                    roi_size = (96, 96, 96)\n",
        "                    sw_batch_size = 4\n",
        "                    val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model)\n",
        "                    val_outputs = post_trans(val_outputs)\n",
        "                    value, _ = dice_metric(y_pred=val_outputs, y=val_labels)\n",
        "                    metric_count += len(value)\n",
        "                    metric_sum += value.item() * len(value)\n",
        "                metric = metric_sum / metric_count\n",
        "                metric_values.append(metric)\n",
        "                if metric > best_metric:\n",
        "                    best_metric = metric\n",
        "                    best_metric_epoch = epoch + 1\n",
        "                    torch.save(model.state_dict(), \"best_metric_model_segmentation3d_array.pth\")\n",
        "                    print(\"saved new best metric model\")\n",
        "                print(\n",
        "                    \"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}\".format(\n",
        "                        epoch + 1, metric, best_metric, best_metric_epoch\n",
        "                    )\n",
        "                )\n",
        "                writer.add_scalar(\"val_mean_dice\", metric, epoch + 1)\n",
        "                # plot the last model output as GIF image in TensorBoard with the corresponding image and label\n",
        "                plot_2d_or_3d_image(val_images, epoch + 1, writer, index=0, tag=\"image\")\n",
        "                plot_2d_or_3d_image(val_labels, epoch + 1, writer, index=0, tag=\"label\")\n",
        "                plot_2d_or_3d_image(val_outputs, epoch + 1, writer, index=0, tag=\"output\")\n",
        "\n",
        "print(f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\")\n",
        "writer.close()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------\n",
            "epoch 1/5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-a3156ff70756>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mepoch_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mbatch_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m             \u001b[0mstep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    436\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1083\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m                 \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1085\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1086\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1087\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1109\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1110\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1111\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1112\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    426\u001b[0m             \u001b[0;31m# have message field\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Caught RuntimeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.6/dist-packages/monai/transforms/utils.py\", line 361, in apply_transform\n    return transform(data)\n  File \"/usr/local/lib/python3.6/dist-packages/monai/transforms/intensity/array.py\", line 144, in __call__\n    return rescale_array(img, self.minv, self.maxv, img.dtype)\nAttributeError: 'str' object has no attribute 'dtype'\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.6/dist-packages/monai/transforms/utils.py\", line 361, in apply_transform\n    return transform(data)\n  File \"/usr/local/lib/python3.6/dist-packages/monai/transforms/compose.py\", line 236, in __call__\n    input_ = apply_transform(_transform, input_)\n  File \"/usr/local/lib/python3.6/dist-packages/monai/transforms/utils.py\", line 363, in apply_transform\n    raise RuntimeError(f\"applying transform {transform}\") from e\nRuntimeError: applying transform <monai.transforms.intensity.array.ScaleIntensity object at 0x7f03518eb400>\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"/usr/local/lib/python3.6/dist-packages/monai/data/dataset.py\", line 915, in __getitem__\n    return self.dataset[index]\n  File \"/usr/local/lib/python3.6/dist-packages/monai/data/dataset.py\", line 812, in __getitem__\n    data.extend(to_list(dataset[index]))\n  File \"/usr/local/lib/python3.6/dist-packages/monai/data/dataset.py\", line 69, in __getitem__\n    data = apply_transform(self.transform, data)\n  File \"/usr/local/lib/python3.6/dist-packages/monai/transforms/utils.py\", line 363, in apply_transform\n    raise RuntimeError(f\"applying transform {transform}\") from e\nRuntimeError: applying transform <monai.transforms.compose.Compose object at 0x7f03518eb518>\n"
          ]
        }
      ]
    }
  ]
}