modules/integrate_3rd_party_transforms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrate 3rd party transforms into a MONAI program\n",
    "\n",
    "This tutorial shows how to integrate 3rd party transforms into a MONAI program.  \n",
    "Mainly showing transforms from `BatchGenerator`, `TorchIO`, `Rising` and `ITK`.\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/modules/integrate_3rd_party_transforms.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "!python -c \"import batchgenerators\" || pip install -q batchgenerators==0.20.1\n",
    "!python -c \"import itk\" || pip install -q itk==5.1.0\n",
    "!python -c \"import rising\" || pip install -q rising==0.2.0\n",
    "!python -c \"import torchio\" || pip install -q torchio==0.17.21\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you use TorchIO for your research, please cite the following paper:\n",
      "Pérez-García et al., TorchIO: a Python library for efficient loading,\n",
      "preprocessing, augmentation and patch-based sampling of medical images\n",
      "in deep learning. Link: https://arxiv.org/abs/2003.04696\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from monai.utils import first, set_determinism\n",
    "from monai.transforms import (\n",
    "    AddChanneld,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    SqueezeDimd,\n",
    "    EnsureTyped,\n",
    "    adaptor,\n",
    ")\n",
    "from monai.data import DataLoader, Dataset\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import torchio\n",
    "import rising.transforms\n",
    "import rising.random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itk\n",
    "from batchgenerators.transforms.color_transforms import \\\n",
    "    ContrastAugmentationTransform\n",
    "import tempfile\n",
    "import shutil\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.6.0rc1+23.gc6793fd0\n",
      "Numpy version: 1.20.3\n",
      "Pytorch version: 1.9.0a0+c3d40fd\n",
      "MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
      "MONAI rev id: c6793fd0f316a448778d0047664aaf8c1895fe1c\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.5\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 7.0.0\n",
      "Tensorboard version: 2.5.0\n",
      "gdown version: 3.13.0\n",
      "TorchVision version: 0.10.0a0\n",
      "ITK version: 5.1.2\n",
      "tqdm version: 4.53.0\n",
      "lmdb version: 1.2.1\n",
      "psutil version: 5.8.0\n",
      "pandas version: 1.1.4\n",
      "einops version: 0.3.0\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\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": {},
   "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": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspace/data/medical\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": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the dataset.  \n",
    "The dataset comes from http://medicaldecathlon.com/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup MONAI transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "monai_transforms = [\n",
    "    LoadImaged(keys=[\"image\", \"label\"]),\n",
    "    AddChanneld(keys=[\"image\", \"label\"]),\n",
    "    Spacingd(keys=[\"image\", \"label\"], pixdim=(\n",
    "        1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
    "    Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "    ScaleIntensityRanged(keys=[\"image\"], a_min=-57,\n",
    "                         a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
    "    CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup BatchGenerator transforms\n",
    "\n",
    "Note:\n",
    "1. BatchGenerator requires the arg is `**data`, can't compose with MONAI transforms directly, need `adaptor`.\n",
    "1. BatchGenerator requires data shape is [B, C, H, W, D], MONAI requires [C, H, W, D]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_generator_transforms = ContrastAugmentationTransform(\n",
    "    data_key=\"image\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup TorchIO transforms\n",
    "\n",
    "Note:\n",
    "1. The TorchIO transforms can support MONAI dict input data directly.\n",
    "1. It can handle PyTorch Tensor data(shape: [C, H, W, D]), so used it to handle Tensor in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torchio_transforms = torchio.transforms.RescaleIntensity(\n",
    "    out_min_max=(0.0, 1.0), percentiles=(0.05, 99.5), keys=[\"image\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Rising transforms\n",
    "\n",
    "Note:\n",
    "1. Rising inherits from PyTorch `nn.Module`, expected input data type is PyTorch Tensor, so can only work after `EnsureType`.\n",
    "1. Rising requires data shape is [B, C, H, W, D], MONAI requires [C, H, W, D].\n",
    "1. Rising requires the arg is `**data`, need `adaptor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rising_transforms = rising.transforms.Mirror(\n",
    "    dims=rising.random.DiscreteParameter((0, 1, 2)), keys=[\"image\", \"label\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup ITK transforms\n",
    "\n",
    "Note:\n",
    "1. ITK transform function API has several args(not only `data`), need to set args in wrapper before Compose.\n",
    "1. If input data is Numpy, ITK can't support dict type, need wrapper to convert the format.\n",
    "1. ITK expects input shape [H, W, [D]], so handle every channel and stack the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def itk_transforms(x):\n",
    "    smoothed = []\n",
    "    for channel in x[\"image\"]:\n",
    "        smoothed.append(itk.median_image_filter(channel, radius=2))\n",
    "    x[\"image\"] = np.stack(smoothed)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compose all transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = Compose(\n",
    "    monai_transforms\n",
    "    + [\n",
    "        itk_transforms,\n",
    "        # add another dim as BatchGenerator and\n",
    "        # Rising expects shape [B, C, H, W, D]\n",
    "        AddChanneld(keys=[\"image\", \"label\"]),\n",
    "        adaptor(batch_generator_transforms, {\"image\": \"image\"}),\n",
    "        EnsureTyped(keys=[\"image\", \"label\"]),\n",
    "        adaptor(rising_transforms, {\"image\": \"image\", \"label\": \"label\"}),\n",
    "        # squeeze shape from [B, C, H, W, D] to\n",
    "        # [C, H, W, D] for TorchIO transforms\n",
    "        SqueezeDimd(keys=[\"image\", \"label\"], dim=0),\n",
    "        torchio_transforms,\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check transforms in DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: torch.Size([329, 282, 136]), label shape: torch.Size([329, 282, 136])\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=data_dicts, transform=transform)\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": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}