3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2UwQsv99wCMB"
   },
   "source": [
    "# Spleen 3D segmentation with MONAI\n",
    "\n",
    "This tutorial shows how to integrate MONAI into an existing PyTorch medical DL program.\n",
    "\n",
    "And easily use below features:\n",
    "1. Transforms for dictionary format data.\n",
    "1. Load Nifti image with metadata.\n",
    "1. Add channel dim to the data if no channel dimension.\n",
    "1. Scale medical image intensity with expected range.\n",
    "1. Crop out a batch of balanced images based on positive / negative label ratio.\n",
    "1. Cache IO and transforms to accelerate training and validation.\n",
    "1. 3D UNet model, Dice loss function, Mean Dice metric for 3D segmentation task.\n",
    "1. Sliding window inference method.\n",
    "1. Deterministic training for reproducibility.\n",
    "\n",
    "The Spleen dataset can be downloaded from http://medicaldecathlon.com/.\n",
    "\n",
    "![spleen](http://medicaldecathlon.com/img/spleen0.png)\n",
    "\n",
    "Target: Spleen  \n",
    "Modality: CT  \n",
    "Size: 61 3D volumes (41 Training + 20 Testing)  \n",
    "Source: Memorial Sloan Kettering Cancer Center  \n",
    "Challenge: Large ranging foreground size\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/3d_segmentation/spleen_segmentation_3d.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kSmTplwYwCMF"
   },
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DxWItTn7wCMG",
    "outputId": "18013027-e54a-4839-99db-26b28ecf22c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"<string>\", line 1, in <module>\n",
      "ModuleNotFoundError: No module named 'monai'\n",
      "\u001b[K     |████████████████████████████████| 760 kB 12.4 MB/s \n",
      "\u001b[K     |████████████████████████████████| 251 kB 47.8 MB/s \n",
      "\u001b[?25hTraceback (most recent call last):\n",
      "  File \"<string>\", line 1, in <module>\n",
      "ModuleNotFoundError: No module named 'aim'\n",
      "\u001b[K     |████████████████████████████████| 22.7 MB 4.6 MB/s \n",
      "\u001b[K     |████████████████████████████████| 181 kB 37.6 MB/s \n",
      "\u001b[K     |████████████████████████████████| 55 kB 2.4 MB/s \n",
      "\u001b[K     |████████████████████████████████| 3.6 MB 45.9 MB/s \n",
      "\u001b[K     |████████████████████████████████| 280 kB 47.3 MB/s \n",
      "\u001b[K     |████████████████████████████████| 503 kB 47.1 MB/s \n",
      "\u001b[K     |████████████████████████████████| 51 kB 337 kB/s \n",
      "\u001b[K     |████████████████████████████████| 4.0 MB 41.5 MB/s \n",
      "\u001b[K     |████████████████████████████████| 210 kB 51.3 MB/s \n",
      "\u001b[K     |████████████████████████████████| 5.1 MB 37.8 MB/s \n",
      "\u001b[K     |████████████████████████████████| 4.3 MB 37.1 MB/s \n",
      "\u001b[K     |████████████████████████████████| 53 kB 1.5 MB/s \n",
      "\u001b[K     |████████████████████████████████| 78 kB 6.1 MB/s \n",
      "\u001b[K     |████████████████████████████████| 60 kB 6.8 MB/s \n",
      "\u001b[K     |████████████████████████████████| 10.9 MB 16.6 MB/s \n",
      "\u001b[K     |████████████████████████████████| 63 kB 1.5 MB/s \n",
      "\u001b[K     |████████████████████████████████| 58 kB 5.7 MB/s \n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[gdown, nibabel, tqdm, ignite]\"\n",
    "!python -c \"import aim\" || pip install -q aim\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZejZZonxwCMH",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from monai.utils import first, set_determinism\n",
    "from monai.transforms import (\n",
    "    AsDiscrete,\n",
    "    AsDiscreted,\n",
    "    EnsureChannelFirstd,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    SaveImaged,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    EnsureTyped,\n",
    "    EnsureType,\n",
    "    Invertd,\n",
    ")\n",
    "from monai.handlers.utils import from_engine\n",
    "from monai.networks.nets import UNet\n",
    "from monai.networks.layers import Norm\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.losses import DiceLoss\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.data import CacheDataset, DataLoader, Dataset, decollate_batch\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import aim\n",
    "from aim.pytorch import track_gradients_dists, track_params_dists\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import tempfile\n",
    "import shutil\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FvkmcSGHwCMI"
   },
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pZSoIBKEwCMI",
    "outputId": "d592b04e-87cc-4cda-a4e6-82c659d5dec8",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.9.dev2210\n",
      "Numpy version: 1.21.5\n",
      "Pytorch version: 1.10.0+cu111\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
      "MONAI rev id: 1a660e6a7a50e985af5ff76b559baab44175438c\n",
      "MONAI __file__: /usr/local/lib/python3.7/dist-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.8\n",
      "Nibabel version: 3.0.2\n",
      "scikit-image version: 0.18.3\n",
      "Pillow version: 7.1.2\n",
      "Tensorboard version: 2.8.0\n",
      "gdown version: 4.2.2\n",
      "TorchVision version: 0.11.1+cu111\n",
      "tqdm version: 4.63.0\n",
      "lmdb version: 0.99\n",
      "psutil version: 5.4.8\n",
      "pandas version: 1.3.5\n",
      "einops version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow 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": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OvWUJiLUwCMJ"
   },
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vaeW5JrmwCMK",
    "outputId": "e7148532-53e7-430a-a68b-88854343f6ca",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/tmp05l4_e7j\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NW2wdPO7wCMN"
   },
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the dataset.  \n",
    "The dataset comes from http://medicaldecathlon.com/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1_4y2EtCwCMN",
    "outputId": "2a874ea6-b270-4032-b072-268082a9e651",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Task09_Spleen.tar: 1.50GB [01:26, 18.6MB/s]                            "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-03-11 18:20:08,035 - INFO - Downloaded: /tmp/tmp05l4_e7j/Task09_Spleen.tar\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-03-11 18:20:11,495 - INFO - Verified 'Task09_Spleen.tar', md5: 410d4a301da4e5b2f6f86ec3ddba524e.\n",
      "2022-03-11 18:20:11,497 - INFO - Writing into directory: /tmp/tmp05l4_e7j.\n"
     ]
    }
   ],
   "source": [
    "resource = \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\"\n",
    "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
    "data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
    "if not os.path.exists(data_dir):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w3EPRPqBwCMN"
   },
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lVZr7-kBwCMO"
   },
   "outputs": [],
   "source": [
    "train_images = sorted(\n",
    "    glob.glob(os.path.join(data_dir, \"imagesTr\", \"*.nii.gz\")))\n",
    "train_labels = sorted(\n",
    "    glob.glob(os.path.join(data_dir, \"labelsTr\", \"*.nii.gz\")))\n",
    "data_dicts = [\n",
    "    {\"image\": image_name, \"label\": label_name}\n",
    "    for image_name, label_name in zip(train_images, train_labels)\n",
    "]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q1Wi6EtAwCMO"
   },
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dr8HRsffwCMO"
   },
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "13ZnlKGCwCMO"
   },
   "source": [
    "## Setup transforms for training and validation\n",
    "\n",
    "Here we use several transforms to augment the dataset:\n",
    "1. `LoadImaged` loads the spleen CT images and labels from NIfTI format files.\n",
    "1. `AddChanneld` as the original data doesn't have channel dim, add 1 dim to construct \"channel first\" shape.\n",
    "1. `Spacingd` adjusts the spacing by `pixdim=(1.5, 1.5, 2.)` based on the affine matrix.\n",
    "1. `Orientationd` unifies the data orientation based on the affine matrix.\n",
    "1. `ScaleIntensityRanged` extracts intensity range [-57, 164] and scales to [0, 1].\n",
    "1. `CropForegroundd` removes all zero borders to focus on the valid body area of the images and labels.\n",
    "1. `RandCropByPosNegLabeld` randomly crop patch samples from big image based on pos / neg ratio.  \n",
    "The image centers of negative samples must be in valid body area.\n",
    "1. `RandAffined` efficiently performs `rotate`, `scale`, `shear`, `translate`, etc. together based on PyTorch affine transform.\n",
    "1. `EnsureTyped` converts the numpy array to PyTorch Tensor for further steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jf7siKPOwCMO"
   },
   "outputs": [],
   "source": [
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
    "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
    "        ScaleIntensityRanged(\n",
    "            keys=[\"image\"], a_min=-57, a_max=164,\n",
    "            b_min=0.0, b_max=1.0, clip=True,\n",
    "        ),\n",
    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "        RandCropByPosNegLabeld(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            label_key=\"label\",\n",
    "            spatial_size=(96, 96, 96),\n",
    "            pos=1,\n",
    "            neg=1,\n",
    "            num_samples=4,\n",
    "            image_key=\"image\",\n",
    "            image_threshold=0,\n",
    "        ),\n",
    "        # user can also add other random transforms\n",
    "        # RandAffined(\n",
    "        #     keys=['image', 'label'],\n",
    "        #     mode=('bilinear', 'nearest'),\n",
    "        #     prob=1.0, spatial_size=(96, 96, 96),\n",
    "        #     rotate_range=(0, 0, np.pi/15),\n",
    "        #     scale_range=(0.1, 0.1, 0.1)),\n",
    "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")\n",
    "val_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
    "            1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
    "        ScaleIntensityRanged(\n",
    "            keys=[\"image\"], a_min=-57, a_max=164,\n",
    "            b_min=0.0, b_max=1.0, clip=True,\n",
    "        ),\n",
    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "au4rmQfDwCMP"
   },
   "source": [
    "## Check transforms in DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "id": "qqcFPuVkwCMP",
    "outputId": "4189428e-4569-4453-e379-df4466208c85",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: torch.Size([226, 157, 113]), label shape: torch.Size([226, 157, 113])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "check_loader = DataLoader(check_ds, batch_size=1)\n",
    "check_data = first(check_loader)\n",
    "image, label = (check_data[\"image\"][0][0], check_data[\"label\"][0][0])\n",
    "print(f\"image shape: {image.shape}, label shape: {label.shape}\")\n",
    "# plot the slice [:, :, 80]\n",
    "plt.figure(\"check\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"image\")\n",
    "plt.imshow(image[:, :, 80], cmap=\"gray\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"label\")\n",
    "plt.imshow(label[:, :, 80])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D0_EHJ7FwCMQ"
   },
   "source": [
    "## Define CacheDataset and DataLoader for training and validation\n",
    "\n",
    "Here we use CacheDataset to accelerate training and validation process, it's 10x faster than the regular Dataset.  \n",
    "To achieve best performance, set `cache_rate=1.0` to cache all the data, if memory is not enough, set lower value.  \n",
    "Users can also set `cache_num` instead of `cache_rate`, will use the minimum value of the 2 settings.  \n",
    "And set `num_workers` to enable multi-threads during caching.  \n",
    "If want to to try the regular Dataset, just change to use the commented code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kKA4gboPwCMQ",
    "outputId": "2496df99-8445-4c70-a3b1-721f9e552b34",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading dataset: 100%|██████████| 32/32 [01:40<00:00,  3.13s/it]\n",
      "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  cpuset_checked))\n",
      "Loading dataset: 100%|██████████| 9/9 [00:21<00:00,  2.42s/it]\n"
     ]
    }
   ],
   "source": [
    "train_ds = CacheDataset(\n",
    "    data=train_files, transform=train_transforms,\n",
    "    cache_rate=1.0, num_workers=4)\n",
    "# train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)\n",
    "\n",
    "# use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "# to generate 2 x 4 images for network training\n",
    "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4)\n",
    "\n",
    "val_ds = CacheDataset(\n",
    "    data=val_files, transform=val_transforms, cache_rate=1.0, num_workers=4)\n",
    "# val_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=1, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nOgy1x1BwCMQ"
   },
   "source": [
    "## Create Model, Loss, Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VM-5g2bmwCMQ"
   },
   "outputs": [],
   "source": [
    "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n",
    "device = torch.device(\"cuda:0\")\n",
    "\n",
    "UNet_meatdata = dict(\n",
    "    spatial_dims=3,\n",
    "    in_channels=1,\n",
    "    out_channels=2,\n",
    "    channels=(16, 32, 64, 128, 256),\n",
    "    strides=(2, 2, 2, 2),\n",
    "    num_res_units=2,\n",
    "    norm=Norm.BATCH\n",
    ")\n",
    "\n",
    "model = UNet(**UNet_meatdata).to(device)\n",
    "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "loss_type = \"DiceLoss\"\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "dice_metric = DiceMetric(include_background=False, reduction=\"mean\")\n",
    "\n",
    "Optimizer_metadata = {}\n",
    "for ind, param_group in enumerate(optimizer.param_groups):\n",
    "    optim_meta_keys = list(param_group.keys())\n",
    "    Optimizer_metadata[f'param_group_{ind}'] = {key: value for (key, value) in param_group.items() if 'params' not in key}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4nD1pAY-wCMR"
   },
   "source": [
    "## Execute a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KayxFseYwCMR",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "max_epochs = 600\n",
    "val_interval = 10\n",
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=2)])\n",
    "post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])\n",
    "\n",
    "# initialize a new Aim Run\n",
    "aim_run = aim.Run()\n",
    "# log model metadata\n",
    "aim_run['UNet_meatdata'] = UNet_meatdata\n",
    "# log optimizer metadata\n",
    "aim_run['Optimizer_metadata'] = Optimizer_metadata\n",
    "\n",
    "slice_to_track = 80\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    print(\"-\" * 10)\n",
    "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "    for batch_data in train_loader:\n",
    "        step += 1\n",
    "        inputs, labels = (\n",
    "            batch_data[\"image\"].to(device),\n",
    "            batch_data[\"label\"].to(device),\n",
    "        )\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",
    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size}, \"\n",
    "            f\"train_loss: {loss.item():.4f}\")\n",
    "        # track batch loss metric\n",
    "        aim_run.track(loss.item(), name=\"batch_loss\", context={'type':loss_type})\n",
    "        \n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append(epoch_loss)\n",
    "    \n",
    "    # track epoch loss metric\n",
    "    aim_run.track(epoch_loss, name=\"epoch_loss\", context={'type':loss_type})\n",
    "\n",
    "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        if (epoch + 1) % val_interval * 2 == 0:\n",
    "            # track model params and gradients\n",
    "            track_params_dists(model,aim_run)\n",
    "            # THIS SEGMENT TAKES RELATIVELY LONG (Advise Against it)\n",
    "            track_gradients_dists(model, aim_run)\n",
    "        \n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for index, val_data in enumerate(val_loader):\n",
    "                val_inputs, val_labels = (\n",
    "                    val_data[\"image\"].to(device),\n",
    "                    val_data[\"label\"].to(device),\n",
    "                )\n",
    "                roi_size = (160, 160, 160)\n",
    "                sw_batch_size = 4\n",
    "                val_outputs = sliding_window_inference(\n",
    "                    val_inputs, roi_size, sw_batch_size, model)\n",
    "                \n",
    "                # tracking input, label and output images with Aim\n",
    "                output = torch.argmax(val_outputs, dim=1)[0, :, :, slice_to_track].float()\n",
    "                \n",
    "                aim_run.track(aim.Image(val_inputs[0, 0, :, :, slice_to_track], \\\n",
    "                                        caption=f'Input Image: {index}'), \\\n",
    "                               name='validation', context={'type':'input'})\n",
    "                aim_run.track(aim.Image(val_labels[0, 0, :, :, slice_to_track], \\\n",
    "                                        caption=f'Label Image: {index}'), \\\n",
    "                               name='validation', context={'type':'label'})\n",
    "                aim_run.track(aim.Image(output, caption=f'Predicted Label: {index}'), \\\n",
    "                               name = 'predictions', context={'type':'labels'}) \n",
    "\n",
    "                val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "                val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "                # compute metric for current iteration\n",
    "                dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "            \n",
    "            # aggregate the final mean dice result\n",
    "            metric = dice_metric.aggregate().item()\n",
    "            # track val metric\n",
    "            aim_run.track(metric, name=\"val_metric\", context={'type':loss_type})\n",
    "\n",
    "            # reset the status for next validation round\n",
    "            dice_metric.reset()\n",
    "\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(), os.path.join(\n",
    "                    root_dir, \"best_metric_model.pth\"))\n",
    "                \n",
    "                best_model_log_message = f\"saved new best metric model at the {epoch+1}th epoch\"\n",
    "                aim_run.track(aim.Text(best_model_log_message), name='best_model_log_message', epoch=epoch+1)\n",
    "                print(best_model_log_message)\n",
    "                \n",
    "            message1 = f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "            message2 = f\"\\nbest mean dice: {best_metric:.4f} \"\n",
    "            message3 = f\"at epoch: {best_metric_epoch}\"\n",
    "    \n",
    "            aim_run.track(aim.Text(message1 +\"\\n\" + message2 + message3), name='epoch_summary', epoch=epoch+1)\n",
    "            print(message1, message2, message3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d1WMn7DFKkbV"
   },
   "outputs": [],
   "source": [
    "# finalize Aim Run\n",
    "aim_run.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ygo9hrWswCMR",
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\n",
    "    f\"train completed, best_metric: {best_metric:.4f} \"\n",
    "    f\"at epoch: {best_metric_epoch}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "90la_Hq8wCMR"
   },
   "source": [
    "## Run Aim UI to deeply explore tracked insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jGhCvBg-wCMS"
   },
   "outputs": [],
   "source": [
    "%load_ext aim\n",
    "%aim up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zV7fV0CIwCMS"
   },
   "source": [
    "Once the above cell is executed, you will see the Aim UI running in output cell\n",
    "\n",
    "![Aim UI](https://user-images.githubusercontent.com/13848158/156644374-ba04963f-4f63-4fb9-b3ef-4d4e1ae521cc.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KRw5pgLiwCMS"
   },
   "source": [
    "## Explore the loss and metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MeX1wBjXwCMS"
   },
   "source": [
    "Compare metrics curves with Metrics Explorer - group and aggregate by any hyperparameter to easily compare training runs\n",
    "\n",
    "![Metrics Explorer](https://user-images.githubusercontent.com/13848158/156642623-8cf4911d-bed2-42b8-9f39-374f8d31def8.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zGZ5vozGwCMS"
   },
   "source": [
    "## Compare and analyze model outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mZIUa0aNwCMS"
   },
   "source": [
    "Compare models of different runs with input images and labels\n",
    "\n",
    "![Images Explorer](https://user-images.githubusercontent.com/13848158/156642615-c003fb3c-9f37-40f4-b499-ee6623db59ef.jpg)\n",
    "\n",
    "![Images Explorer](https://user-images.githubusercontent.com/13848158/156642618-0c0c380a-75aa-45b1-b431-149f735b3fde.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uZKhs2DFwCMS"
   },
   "source": [
    "## Evaluation on original image spacings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ws5wpqPlwCMT"
   },
   "outputs": [],
   "source": [
    "val_org_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "        Spacingd(keys=[\"image\"], pixdim=(\n",
    "            1.5, 1.5, 2.0), mode=\"bilinear\"),\n",
    "        Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
    "        ScaleIntensityRanged(\n",
    "            keys=[\"image\"], a_min=-57, a_max=164,\n",
    "            b_min=0.0, b_max=1.0, clip=True,\n",
    "        ),\n",
    "        CropForegroundd(keys=[\"image\"], source_key=\"image\"),\n",
    "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_org_ds = Dataset(\n",
    "    data=val_files, transform=val_org_transforms)\n",
    "val_org_loader = DataLoader(val_org_ds, batch_size=1, num_workers=4)\n",
    "\n",
    "post_transforms = Compose([\n",
    "    EnsureTyped(keys=\"pred\"),\n",
    "    Invertd(\n",
    "        keys=\"pred\",\n",
    "        transform=val_org_transforms,\n",
    "        orig_keys=\"image\",\n",
    "        meta_keys=\"pred_meta_dict\",\n",
    "        orig_meta_keys=\"image_meta_dict\",\n",
    "        meta_key_postfix=\"meta_dict\",\n",
    "        nearest_interp=False,\n",
    "        to_tensor=True,\n",
    "    ),\n",
    "    AsDiscreted(keys=\"pred\", argmax=True, to_onehot=2),\n",
    "    AsDiscreted(keys=\"label\", to_onehot=2),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JTkKUwRGwCMT"
   },
   "outputs": [],
   "source": [
    "model.load_state_dict(torch.load(\n",
    "    os.path.join(root_dir, \"best_metric_model.pth\")))\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    for val_data in val_org_loader:\n",
    "        val_inputs = val_data[\"image\"].to(device)\n",
    "        roi_size = (160, 160, 160)\n",
    "        sw_batch_size = 4\n",
    "        val_data[\"pred\"] = sliding_window_inference(\n",
    "            val_inputs, roi_size, sw_batch_size, model)\n",
    "        val_data = [post_transforms(i) for i in decollate_batch(val_data)]\n",
    "        val_outputs, val_labels = from_engine([\"pred\", \"label\"])(val_data)\n",
    "        # compute metric for current iteration\n",
    "        dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "    # aggregate the final mean dice result\n",
    "    metric_org = dice_metric.aggregate().item()\n",
    "    # reset the status for next validation round\n",
    "    dice_metric.reset()\n",
    "\n",
    "print(\"Metric on original image spacing: \", metric_org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "chILRaduwCMT"
   },
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yuCFCxOcwCMT"
   },
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
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