UNETR_LiTS_segmentation_3d.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Qw_ZkFd2KO3X"
      },
      "source": [
        "## UNETR on Liver tumor 3D segmentation \n",
        "\n",
        "This notebook code is developed as part of EECS 545 course project at the University of Michigan\n",
        "We have taken reference of code from [link](https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/brats_segmentation_3d.ipynb)\n",
        "\n",
        "## Multi-labels segmentation pipeline\n",
        "1. Setting up of environment with various imports from MONAI library and Pytorch\n",
        "1. Defining custom Transforms for data manipulation for ease of training for both liver & tumor segmentation.\n",
        "1. Setting MONAI based transforms for Data augmentation.\n",
        "1. Loading Nifti based data (images + segmentations), using the transforms defined, for training & validation.\n",
        "1. Visualizing some of the data images & their corresponding segmentation masks.\n",
        "1. Loading **UNETR model** from MONAI lib\n",
        "1. Setting Dice loss function, Adam Optimizer, and starting a deterministic training for reproducibility.\n",
        "1. Plotting loss and dice metrics.\n",
        "1. Evaluating the best model weights.\n",
        "\n",
        "## Dataset\n",
        "The dataset comes from https://www.kaggle.com/datasets/andrewmvd/liver-tumor-segmentation?resource=download . \n",
        "Target: Liver and Tumor segmentation  \n",
        "Modality: CT Scan  \n",
        "Size: 131 3D volumes (101 Training + 30 Validation)  \n",
        "Source: LiTS 2017 dataset.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ofxe-2ehKO3b"
      },
      "source": [
        "## Setup environment, imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeaG2jRbKO3b",
        "outputId": "e20f0a0d-438a-4f89-8fe4-ffe4ef1bc6aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MONAI version: 1.2.dev2315\n",
            "Numpy version: 1.23.5\n",
            "Pytorch version: 2.0.0+cu118\n",
            "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
            "MONAI rev id: d8703e4a1f7016706bb56e1bbe0a22693d5757c6\n",
            "MONAI __file__: c:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\monai\\__init__.py\n",
            "\n",
            "Optional dependencies:\n",
            "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "Nibabel version: 5.1.0\n",
            "scikit-image version: 0.20.0\n",
            "Pillow version: 9.3.0\n",
            "Tensorboard version: 2.12.1\n",
            "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "TorchVision version: 0.15.1+cu118\n",
            "tqdm version: 4.65.0\n",
            "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "psutil version: 5.9.4\n",
            "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "einops version: 0.6.0\n",
            "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "\n",
            "For details about installing the optional dependencies, please visit:\n",
            "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import time\n",
        "import argparse\n",
        "import warnings\n",
        "\n",
        "from glob import glob\n",
        "import numpy as np\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from monai.networks.nets import SegResNet, UNet, UNETR\n",
        "from monai.config import KeysCollection, print_config\n",
        "from monai.networks.layers import Norm\n",
        "from monai.data import DataLoader, CacheDataset, decollate_batch\n",
        "from monai.utils import set_determinism\n",
        "from monai.losses import DiceLoss\n",
        "from monai.metrics import DiceMetric\n",
        "from monai.inferers import sliding_window_inference\n",
        "from monai.transforms import(\n",
        "    Compose,\n",
        "    LoadImaged,\n",
        "    ToTensord,\n",
        "    Spacingd,\n",
        "    Orientationd,\n",
        "    ScaleIntensityRanged,\n",
        "    EnsureChannelFirstd,\n",
        "    RandSpatialCropd,\n",
        "    Activations,\n",
        "    AsDiscrete,\n",
        "    CropForegroundd,\n",
        "    Resized,\n",
        "    MapTransform,\n",
        "    RandFlipd,\n",
        "    RandRotated,\n",
        ")\n",
        "\n",
        "print_config()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Define a new transform to convert brain tumor labels\n",
        "\n",
        "Here we convert the multi-classes labels into multi-labels segmentation task in One-Hot format."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "./\n"
          ]
        }
      ],
      "source": [
        "root_dir = './data/nifti_files' \n",
        "model_dir = './Task03_liver'\n",
        "print(root_dir)\n",
        "set_determinism(seed=0)\n",
        "\n",
        "###Customised transform classes   \n",
        "\n",
        "class ForceSyncAffined(MapTransform):\n",
        "    \"\"\"\n",
        "    Forcefully set affines of targets to source's affine\n",
        "    Mainly for fixing bad data points\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, keys: KeysCollection, source_key: str, allow_missing_keys: bool = False) -> None:\n",
        "        super().__init__(keys, allow_missing_keys)\n",
        "        self.source_key = source_key    \n",
        "\n",
        "    def __call__(self, data):\n",
        "        d = dict(data)\n",
        "        assert self.source_key in d, f\"Source key {self.source_key} not in data point.\"\n",
        "        s_data_affine = d[self.source_key].affine\n",
        "        for key in self.key_iterator(d):\n",
        "            d[key].affine = s_data_affine\n",
        "        return d \n",
        "    \n",
        "\n",
        "\"\"\"\n",
        "Convert labels to multi channels based on LiTS classes:\n",
        "label 1 is the liver mask\n",
        "label 2 is the tumor\n",
        "The possible classes are Background, Liver and Tumor.\n",
        "\n",
        "\"\"\"\n",
        "def convert_label_to_liver_classes(label):\n",
        "    \n",
        "    # Liver \n",
        "    class1 = (torch.logical_or(label == 1, label == 2)).float()\n",
        "\n",
        "    # Tumor\n",
        "    class2 = (label == 2).float()\n",
        "\n",
        "    return torch.cat((class1,class2), 1)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Pupd1ergKO3k"
      },
      "source": [
        "## Import Data and setup transforms for training and validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "cEPYMLa3KO3k"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\Lenovo\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\monai\\utils\\deprecate_utils.py:321: FutureWarning: monai.transforms.io.dictionary LoadImaged.__init__:image_only: Current default value of argument `image_only=False` has been deprecated since version 1.1. It will be changed to `image_only=True` in version 1.3.\n",
            "  warn_deprecated(argname, msg, warning_category)\n"
          ]
        }
      ],
      "source": [
        "roi_size=[128, 128, 64]\n",
        "pixdim=(1.5, 1.5, 1.0)\n",
        "a_min=-200\n",
        "a_max=200\n",
        "cache_rate = 0.0\n",
        "\n",
        "root= root_dir\n",
        "path_train_volumes = sorted(glob(os.path.join(root, \"images_full\", \"*.nii\")))\n",
        "path_train_segmentation = sorted(glob(os.path.join(root, \"labels_full\", \"*.nii\")))\n",
        "\n",
        "train_files = [{\"vol\": image_name, \"seg\": label_name} for image_name, label_name in zip(path_train_volumes[0:101], path_train_segmentation[0:101])]\n",
        "val_files = [{\"vol\": image_name, \"seg\": label_name} for image_name, label_name in zip(path_train_volumes[101:131], path_train_segmentation[101:131])]\n",
        "\n",
        "train_transforms = Compose([\n",
        "        LoadImaged(keys=[\"vol\", \"seg\"]),\n",
        "        ForceSyncAffined(keys=[\"seg\"], source_key=\"vol\"),\n",
        "        EnsureChannelFirstd(keys=[\"vol\", \"seg\"]),\n",
        "        ScaleIntensityRanged(keys=[\"vol\"], a_min=a_min, a_max=a_max, b_min=0.0, b_max=1.0, clip=True),\n",
        "        Orientationd(keys=[\"vol\", \"seg\"], axcodes=\"RAS\"),\n",
        "        Spacingd(keys=[\"vol\", \"seg\"], pixdim=pixdim, mode=(\"bilinear\", \"nearest\")),\n",
        "        CropForegroundd(keys=['vol', 'seg'], source_key='vol'),\n",
        "        RandSpatialCropd(keys=[\"vol\", \"seg\"], roi_size=[-1,-1,roi_size[2]], random_size=False),\n",
        "        RandRotated(keys=[\"vol\", \"seg\"], prob=0.5, range_x=[0.2,0.2], mode=['bilinear', 'nearest']),\n",
        "        Resized(keys=[\"vol\", \"seg\"], spatial_size=roi_size),\n",
        "        ToTensord(keys=[\"vol\", \"seg\"]),\n",
        "        ])\n",
        "\n",
        "val_transforms = Compose([\n",
        "        LoadImaged(keys=[\"vol\", \"seg\"]),\n",
        "        ForceSyncAffined(keys=[\"seg\"], source_key=\"vol\"),\n",
        "        EnsureChannelFirstd(keys=[\"vol\", \"seg\"]),\n",
        "        ScaleIntensityRanged(keys=[\"vol\"], a_min=a_min, a_max=a_max,b_min=0.0, b_max=1.0, clip=True),            \n",
        "        Orientationd(keys=[\"vol\", \"seg\"], axcodes=\"RAS\"),\n",
        "        Spacingd(keys=[\"vol\", \"seg\"], pixdim=pixdim, mode=(\"bilinear\", \"nearest\")),\n",
        "        CropForegroundd(keys=['vol', 'seg'], source_key='vol'),\n",
        "        Resized(keys=[\"vol\", \"seg\"], spatial_size=roi_size),\n",
        "        ToTensord(keys=[\"vol\", \"seg\"]),\n",
        "        ])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "lqpqGhugKO3l"
      },
      "source": [
        "## Quickly load data with CacheDataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoIniAwHKO3l",
        "outputId": "4f7a8972-f009-410d-db80-c69fc92a9203"
      },
      "outputs": [],
      "source": [
        "train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=cache_rate)\n",
        "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True)\n",
        "\n",
        "val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=cache_rate)\n",
        "val_loader = DataLoader(val_ds, batch_size=8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mhz9iyyKO3m"
      },
      "source": [
        "## Check data shape and visualize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zI3YrMf5KO3m",
        "outputId": "b458f305-7b05-4906-8863-363ca784ae93"
      },
      "outputs": [],
      "source": [
        "slice_id = 40 #tumor on slice 48 if total slices=64\n",
        "patient = 0\n",
        "print(f\"image shape: {train_ds[patient]['vol'].shape}\")\n",
        "plt.figure(\"image\", (24, 6))\n",
        "for i in range(1):\n",
        "    plt.subplot(1, 4, i + 1)\n",
        "    plt.title(f\"image channel {i}\")\n",
        "    plt.imshow(train_ds[patient][\"vol\"][0, :, :, slice_id].detach().cpu(),  cmap=\"gray\") #\n",
        "    \n",
        "plt.show()\n",
        "# also visualize the label corresponding to this image\n",
        "print(f\"label shape: {train_ds[patient]['seg'].shape}\")\n",
        "plt.figure(\"label\", (24, 6))\n",
        "\n",
        "for i in range(train_ds[patient]['seg'].shape[0]):\n",
        "    plt.subplot(1, 3, i + 1)\n",
        "    plt.title(f\"label channel {i}\")\n",
        "    plt.imshow(train_ds[patient][\"seg\"][i, :, :, slice_id].detach().cpu())\n",
        "    \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RQaf8mQQPYr"
      },
      "source": [
        "## Proposed transormer based model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdUy8cdpQO4-",
        "outputId": "9b4fa8ca-dc17-4d50-d277-4d69ce6d33d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Parameters in millions: 92.814562\n"
          ]
        }
      ],
      "source": [
        "from monai.networks.nets import UNETR\n",
        "\n",
        "# Set device\n",
        "device = device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "# Use amp to accelerate training\n",
        "scaler = torch.cuda.amp.GradScaler()\n",
        "# Enable cuDNN benchmark\n",
        "torch.backends.cudnn.benchmark = True\n",
        "\n",
        "num_heads = 12 # 12 normally \n",
        "embed_dim= 768 # 768 normally\n",
        "\n",
        "model = UNETR(\n",
        "    in_channels=1,\n",
        "    out_channels=2,\n",
        "    img_size=tuple(roi_size),\n",
        "    feature_size=16,\n",
        "    hidden_size=embed_dim,\n",
        "    mlp_dim=3072,\n",
        "    num_heads=num_heads,\n",
        "    pos_embed=\"perceptron\",\n",
        "    norm_name=\"instance\",\n",
        "    res_block=True,\n",
        "    dropout_rate=0.0,\n",
        "    ).to(device)\n",
        "\n",
        "pytorch_total_params = sum(p.numel() for p in model.parameters())/1000000\n",
        "print('Parameters in millions:',pytorch_total_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTca5QIMKO3n"
      },
      "source": [
        "## Create Model, Loss, Optimizer, Execute a typical PyTorch training process"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fITUTtJKO3n",
        "outputId": "211c0b4e-beda-4b5a-8474-2f5a38cca453",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------\n",
            "epoch 1/5\n",
            "1/1, loss: 0.9683\n",
            "2/1, loss: 0.8954\n",
            "3/1, loss: 0.8955\n",
            "4/1, loss: 0.9511\n",
            "5/1, loss: 0.9053\n",
            "6/1, loss: 0.9688\n",
            "7/1, loss: 0.9654\n",
            "8/1, loss: 0.9456\n",
            "9/1, loss: 0.9748\n",
            "10/1, loss: 0.9599\n",
            "11/1, loss: 0.9436\n",
            "12/1, loss: 0.9287\n",
            "13/1, loss: 0.9564\n",
            "epoch 1 average loss: 0.9430\n",
            "current epoch: 1 current mean dice: 0.0426 liver: 0.0754 tumor: 0.0006\n",
            "best mean dice: 0.0426 at epoch: 1\n",
            "time consumed for epoch 1 is: 1677.2854\n",
            "--------------------\n",
            "epoch 2/5\n",
            "1/1, loss: 0.9423\n",
            "2/1, loss: 0.9398\n",
            "3/1, loss: 0.9342\n",
            "4/1, loss: 0.9206\n",
            "5/1, loss: 0.9373\n",
            "6/1, loss: 0.9014\n",
            "7/1, loss: 0.8981\n",
            "8/1, loss: 0.9210\n",
            "9/1, loss: 0.9575\n",
            "10/1, loss: 0.9417\n",
            "11/1, loss: 0.9357\n",
            "12/1, loss: 0.9469\n",
            "13/1, loss: 0.9382\n",
            "epoch 2 average loss: 0.9319\n",
            "current epoch: 2 current mean dice: 0.0544 liver: 0.0959 tumor: 0.0005\n",
            "best mean dice: 0.0544 at epoch: 2\n",
            "time consumed for epoch 2 is: 1504.0883\n",
            "--------------------\n",
            "epoch 3/5\n",
            "1/1, loss: 0.8581\n",
            "2/1, loss: 0.9773\n",
            "3/1, loss: 0.8829\n",
            "4/1, loss: 0.9170\n",
            "5/1, loss: 0.8695\n",
            "6/1, loss: 0.9460\n",
            "7/1, loss: 0.9021\n",
            "8/1, loss: 0.9147\n",
            "9/1, loss: 0.8820\n",
            "10/1, loss: 0.9638\n",
            "11/1, loss: 0.9036\n",
            "12/1, loss: 0.9477\n",
            "13/1, loss: 0.8818\n",
            "epoch 3 average loss: 0.9113\n",
            "current epoch: 3 current mean dice: 0.0596 liver: 0.1053 tumor: 0.0004\n",
            "best mean dice: 0.0596 at epoch: 3\n",
            "time consumed for epoch 3 is: 1499.1401\n",
            "--------------------\n",
            "epoch 4/5\n",
            "1/1, loss: 0.8948\n",
            "2/1, loss: 0.9060\n",
            "3/1, loss: 0.8654\n",
            "4/1, loss: 0.9410\n",
            "5/1, loss: 0.9670\n",
            "6/1, loss: 0.8220\n",
            "7/1, loss: 0.9499\n",
            "8/1, loss: 0.9178\n",
            "9/1, loss: 0.9088\n",
            "10/1, loss: 0.9111\n",
            "11/1, loss: 0.9196\n",
            "12/1, loss: 0.9209\n",
            "13/1, loss: 0.9603\n",
            "epoch 4 average loss: 0.9142\n",
            "current epoch: 4 current mean dice: 0.0626 liver: 0.1107 tumor: 0.0004\n",
            "best mean dice: 0.0626 at epoch: 4\n",
            "time consumed for epoch 4 is: 1896.3079\n",
            "--------------------\n",
            "epoch 5/5\n",
            "1/1, loss: 0.9725\n",
            "2/1, loss: 0.8997\n",
            "3/1, loss: 0.9220\n",
            "4/1, loss: 0.9504\n",
            "5/1, loss: 0.9019\n",
            "6/1, loss: 0.8897\n",
            "7/1, loss: 0.8977\n",
            "8/1, loss: 0.9314\n",
            "9/1, loss: 0.9002\n",
            "10/1, loss: 0.9006\n",
            "11/1, loss: 0.9365\n",
            "12/1, loss: 0.9146\n",
            "13/1, loss: 0.9580\n",
            "epoch 5 average loss: 0.9212\n",
            "current epoch: 5 current mean dice: 0.0632 liver: 0.1118 tumor: 0.0004\n",
            "best mean dice: 0.0632 at epoch: 5\n",
            "time consumed for epoch 5 is: 2293.8120\n",
            "train completed, best_metric: 0.0632 at epoch: 5, total train time: 8870.6658\n"
          ]
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "from monai.losses import DiceLoss, DiceCELoss\n",
        "\n",
        "max_epochs = 250#50#180\n",
        "val_interval = 5\n",
        "\n",
        "# Create loss, optimizer, lr_schedule and dice metrics\n",
        "loss_function = DiceLoss(to_onehot_y=False, \n",
        "                         sigmoid=True, \n",
        "                         squared_pred=True, \n",
        "                         smooth_nr=0, \n",
        "                         smooth_dr=1e-5)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5, amsgrad=True)\n",
        "lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=max_epochs)\n",
        "dice_metric = DiceMetric(include_background=True, reduction=\"mean\") \n",
        "dice_metric_batch = DiceMetric(include_background=True, reduction=\"mean_batch\") \n",
        "\n",
        "# Transformation for outputs to 2 classes with sigmoid activation\n",
        "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n",
        "\n",
        "# Start training\n",
        "best_metric = -1\n",
        "best_metric_epoch = -1\n",
        "save_loss = []\n",
        "save_metric = []\n",
        "save_metric_liver = []\n",
        "save_metric_tumor = []\n",
        "metric = 0.0\n",
        "metric_liver = 0.0\n",
        "metric_tumor = 0.0\n",
        "\n",
        "train_start = time.time()\n",
        "\n",
        "for epoch in range(max_epochs):\n",
        "    epoch_start = time.time()\n",
        "    print(\"-\" * 20)\n",
        "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
        "    model.train()\n",
        "    epoch_loss = 0\n",
        "    step = 0\n",
        "    batch_num = 0\n",
        "    \n",
        "    # Train from training ds\n",
        "    for batch_data in train_loader:    \n",
        "        batch_num += 1\n",
        "        volume = batch_data[\"vol\"]\n",
        "        label = batch_data[\"seg\"]\n",
        "        label_tf = convert_label_to_liver_classes(label)\n",
        "    \n",
        "        check_label_size = ((label.size(dim=2) == roi_size[0]) and (label.size(dim=3) == roi_size[1]) and (label.size(dim=4) == roi_size[2]))\n",
        "        check_affine =  torch.all(volume.affine == label.affine)\n",
        "        if (check_affine and check_label_size):\n",
        "            volume, label = (volume.to(device), label_tf.to(device))\n",
        "\n",
        "            # Gradient solver and compute training loss\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            with torch.cuda.amp.autocast():  \n",
        "                outputs = model(volume)\n",
        "                train_loss = loss_function(outputs, label) \n",
        "            scaler.scale(train_loss).backward()\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "            epoch_loss += train_loss.item()\n",
        "\n",
        "            step += 1\n",
        "\n",
        "            print(\n",
        "                f\"{batch_num}/{len(train_loader) // train_loader.batch_size}, \"\n",
        "                f\"loss: {train_loss.item():.4f}\")\n",
        "        \n",
        "        else:\n",
        "            print(f\"{batch_num}/{len(train_loader) // train_loader.batch_size}, label shape incorrect, skip training\")\n",
        "    \n",
        "    lr_scheduler.step()\n",
        "    epoch_loss /= step\n",
        "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "    \n",
        "    save_loss.append(epoch_loss)\n",
        "    np.save(os.path.join(model_dir, 'epoch_loss.npy'), save_loss)\n",
        "\n",
        "    if (epoch + 1) % val_interval == 0:\n",
        "        model.eval()\n",
        "        sw_batch_size = 1\n",
        "        with torch.no_grad():\n",
        "            for val_data in val_loader:\n",
        "                val_volume = val_data[\"vol\"]\n",
        "                val_label = val_data[\"seg\"]\n",
        "                val_volume, val_label = (val_volume.to(device), val_label.to(device))\n",
        "\n",
        "                with torch.cuda.amp.autocast():  \n",
        "                    val_outputs = sliding_window_inference(val_volume, roi_size, sw_batch_size, model, overlap=0.5)\n",
        "\n",
        "                # Compute each val metrics \n",
        "                val_outputs_tf = [post_trans(i) for i in decollate_batch(val_outputs)]\n",
        "                val_label_tf = convert_label_to_liver_classes(val_label)\n",
        "                dice_metric(y_pred=val_outputs_tf, y=val_label_tf)\n",
        "                dice_metric_batch(y_pred=val_outputs_tf, y=val_label_tf)      \n",
        "            \n",
        "            # Get mean metrics\n",
        "            metric = dice_metric.aggregate().item()\n",
        "            dice_metric.reset()\n",
        "\n",
        "            # Get batch metrics\n",
        "            metric_val_batch = dice_metric_batch.aggregate()\n",
        "            metric_liver = metric_val_batch[0].item()\n",
        "            metric_tumor = metric_val_batch[1].item()\n",
        "            dice_metric_batch.reset()\n",
        "        \n",
        "    # save metrics\n",
        "    save_metric.append(metric)\n",
        "    save_metric_liver.append(metric_liver)\n",
        "    save_metric_tumor.append(metric_tumor)\n",
        "    np.save(os.path.join(model_dir, 'metric_mean.npy'), save_metric)\n",
        "    np.save(os.path.join(model_dir, 'metric_liver.npy'), save_metric_liver)\n",
        "    np.save(os.path.join(model_dir, 'metric_tumor.npy'), save_metric_tumor)\n",
        "\n",
        "    # save model with best metric\n",
        "    if metric > best_metric:\n",
        "        best_metric = metric\n",
        "        best_metric_epoch = epoch + 1\n",
        "        torch.save(model.state_dict(), \n",
        "                    os.path.join(model_dir, \"best_metric_model.pth\"))\n",
        "    print(\n",
        "        f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
        "        f\" liver: {metric_liver:.4f} tumor: {metric_tumor:.4f}\"\n",
        "        f\"\\nbest mean dice: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
        "    )\n",
        "\n",
        "    print(f\"time consumed for epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}\")\n",
        "\n",
        "# Train complete\n",
        "print(\n",
        "f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch},\"\n",
        "f\" total train time: {(time.time() - train_start):.4f}\"\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "slO6r1ebKO3n"
      },
      "outputs": [],
      "source": [
        "\n",
        "# unet tutorial result: train completed, best_metric: 0.7537  at epoch: 160 # official tutorial\n",
        "# train completed, best_metric: 0.7660 at epoch: 170 # my run\n",
        "\n",
        "# self-attention-cv implementation of unetr\n",
        "# current epoch: 180 current mean dice: 0.7686 tc: 0.8116 wt: 0.8935 et: 0.6065\n",
        "# best mean dice: 0.7686 at epoch: 180\n",
        "\n",
        "# reduced version 50m params\n",
        "# current epoch: 180 current mean dice: 0.7693 tc: 0.8161 wt: 0.8922 et: 0.6057\n",
        "# best mean dice: 0.7693 at epoch: 180\n",
        "\n",
        "# unetr monai implementation\n",
        "# current epoch: 175 current mean dice: 0.7612 tc: 0.8122 wt: 0.8790 et: 0.5982 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpTUwxrIKO3o"
      },
      "source": [
        "## Plot the loss and metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791
        },
        "id": "7cXKOdDSKO3o",
        "outputId": "56c1c2fa-7283-43c0-a7f0-7fcb405702c0"
      },
      "outputs": [],
      "source": [
        "plt.figure(\"train\", (12, 6))\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.title(\"Train dice loss\")\n",
        "x = [i + 1 for i in range(len(save_loss))]\n",
        "y = save_loss\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.title(\"Test metric DICE Mean\")\n",
        "x = [val_interval * (i + 1) for i in range(len(save_metric))]\n",
        "y = save_metric\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.figure(\"train\", (12, 6))\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.title(\"Test metric DICE Liver\")\n",
        "x = [val_interval * (i + 1) for i in range(len(save_metric_liver))]\n",
        "y = save_metric_liver\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.title(\"Test metric DICE Tumor\")\n",
        "x = [val_interval * (i + 1) for i in range(len(save_metric_tumor))]\n",
        "y = save_metric_tumor\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.plot(x, y)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05U7gC15KO3p"
      },
      "source": [
        "## Check best model output with the input image and label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model.load_state_dict(\n",
        "    torch.load(os.path.join(model_dir, \"best_metric_model.pth\"))\n",
        ")\n",
        "model.eval()\n",
        "slice_id = 48\n",
        "sw_batch_size = 1\n",
        "\n",
        "# Transformation for outputs to 2 classes with sigmoid activation\n",
        "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n",
        "\n",
        "with torch.no_grad():\n",
        "    for val_data in val_loader:\n",
        "        val_volume = val_data[\"vol\"]\n",
        "        val_label = val_data[\"seg\"]\n",
        "        val_volume, val_label = (val_volume.to(device), val_label.to(device))\n",
        "\n",
        "        with torch.cuda.amp.autocast():  \n",
        "            val_outputs = sliding_window_inference(val_volume, roi_size, sw_batch_size, model, overlap=0.5)\n",
        "\n",
        "        # Compute each val metrics \n",
        "        val_outputs_tf = [post_trans(i) for i in decollate_batch(val_outputs)]\n",
        "        val_label_tf = convert_label_to_liver_classes(val_label)\n",
        "        # print(\"image.shape\", val_volume.shape)\n",
        "        # print(\"ground truth shape\", val_label_tf[0].shape)\n",
        "        # print(\"UNETR output shape\", val_outputs_tf[0].shape)\n",
        "        \n",
        "        plt.figure(\"output\", (18, 6))\n",
        "        \n",
        "        plt.subplot(1, 3, 1)\n",
        "        plt.title(f\"Image\")\n",
        "        plt.imshow(val_volume[0, 0, :, :,slice_id].detach().cpu(), cmap=\"gray\")\n",
        "\n",
        "        plt.subplot(1, 3, 2)\n",
        "        plt.title(f\"Ground Truth\")\n",
        "        gt_liver_tumor = torch.zeros_like(val_label_tf[0,1, :, :, slice_id])\n",
        "        gt_healthy_liver = val_label_tf[0,0, ..., slice_id] == 1\n",
        "        gt_unhealthy_liver = val_label_tf[0,1, ..., slice_id] == 1\n",
        "        gt_liver_tumor[gt_healthy_liver] = 1\n",
        "        gt_liver_tumor[gt_unhealthy_liver] = 2\n",
        "        plt.imshow(gt_liver_tumor.detach().cpu(), vmin=0, vmax=2)\n",
        "\n",
        "        plt.subplot(1, 3, i + 3)\n",
        "        plt.title(f\"UNETR output\")\n",
        "        pred_liver_tumor = torch.zeros_like(val_outputs_tf[0][0, :, :, slice_id])\n",
        "        pred_healthy_liver = val_outputs_tf[0][0, ..., slice_id] >= 0.5\n",
        "        pred_unhealthy_liver = val_outputs_tf[0][1, ..., slice_id] >= 0.5\n",
        "        pred_liver_tumor[pred_healthy_liver] = 1\n",
        "        pred_liver_tumor[pred_unhealthy_liver] = 2\n",
        "        plt.imshow(pred_liver_tumor.detach().cpu(), vmin=0, vmax=2)\n",
        "        plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}