ModelInference/Lab-2-monaideploy.ipynb

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   "source": [
    "# AWS HealthImaging Integrated with MONAI Deploy App SDK \n",
    "\n",
    "Setup notebook environment using \"PyTorch 1.12 Python 3.8 GPU optimized\" Kernel with \"g4dn.xlarge\" instance type. \n",
    "\n",
    "<img src=\"../Figures/studio_setup_gpu.png\" width=\"600\">\n",
    "\n",
    "\n",
    "In the following sections, we will demonstrate how to create a MONAI Deploy application package using the MONAI Deploy App SDK, which will read data from Amazon HealthLake Imaging\n",
    "\n",
    "## Creating Operators and connecting them in Application class\n",
    "\n",
    "We will implement an application that consists of the new DataLoader Operators:\n",
    "\n",
    "- **AHLIDataLoaderOperator**:\n",
    "    - **Input(json_file)**: a file with Amazon HealthLake Imaging DatastoreId and ImageSetIds to be loaded ([`DataPath`](/modules/_autosummary/monai.deploy.core.domain.DataPath))\n",
    "    - **Output(dicom_study_list)**: a list of DICOM studies in memory (List[[`DICOMStudy`](/modules/_autosummary/monai.deploy.core.domain.DICOMStudy)])\n",
    "\n",
    "\n",
    "\n",
    "This MAP is derived from this tutorial: https://github.com/Project-MONAI/monai-deploy-app-sdk/blob/main/notebooks/tutorials/03_segmentation_app.ipynb\n",
    "\n",
    "## Download and install libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%sh\n",
    "pip install --upgrade -q pip\n",
    "pip install --upgrade -q boto3 botocore\n",
    "pip install -q \"nibabel>=3.2.1\" \"pydicom>=1.4.2\" \"highdicom>=0.18.2\" \"SimpleITK>=2.0.0\" \"typeguard>=2.12.1\" \"itk>=5.3rc4\" \"itkwidgets[all]>=1.0a23\"\n",
    "pip install -q tqdm pathlib2 pylibjpeg-openjpeg \n",
    "pip install --upgrade -q \"monai\" monai-deploy-app-sdk~=0.5.1 \n",
    "pip install --upgrade -q AHItoDICOMInterface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store -r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup imports\n",
    "\n",
    "Let's import necessary classes/decorators to define Application and Operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "from os import path\n",
    "from numpy import uint8\n",
    "import boto3\n",
    "import json\n",
    "import monai.deploy.core as md\n",
    "from pydicom.sr.codedict import codes\n",
    "from monai.deploy.core import Application, resource, Image, IOType\n",
    "from monai.deploy.operators.dicom_seg_writer_operator import DICOMSegmentationWriterOperator, SegmentDescription\n",
    "from monai.deploy.operators.dicom_series_selector_operator import DICOMSeriesSelectorOperator\n",
    "from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator\n",
    "from monai.deploy.operators.monai_bundle_inference_operator import (\n",
    "    BundleConfigNames,\n",
    "    IOMapping,\n",
    "    MonaiBundleInferenceOperator,\n",
    ")\n",
    "logging.basicConfig( level=logging.INFO )\n",
    "logging.getLogger('AHItoDICOMInterface').setLevel(logging.CRITICAL)\n",
    "\n",
    "from src.code.ahi_data_loader_operator import AHIDataLoaderOperator\n",
    "from AHItoDICOMInterface.AHItoDICOM import AHItoDICOM\n",
    "helper = AHItoDICOM()\n",
    "instances = helper.DICOMizeImageSet(datastore_id=datastoreId , image_set_id=next(iter(imageSetIds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Application class\n",
    "\n",
    "Our application class would look like below.\n",
    "\n",
    "It defines `App` class, inheriting [Application](/modules/_autosummary/monai.deploy.core.Application) class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import traceback\n",
    "\n",
    "@resource(cpu=1, gpu=1, memory=\"7Gi\")\n",
    "class AISpleenSegApp(Application):\n",
    "    def __init__(self, ahi_client, *args, **kwargs):\n",
    "        \"\"\"Creates an application instance.\"\"\"\n",
    "        self._logger = logging.getLogger(\"{}.{}\".format(__name__, type(self).__name__))\n",
    "        self.ahi_client = ahi_client\n",
    "        super().__init__(*args, **kwargs)\n",
    "\n",
    "    def run(self, *args, **kwargs):\n",
    "        # This method calls the base class to run. Can be omitted if simply calling through.\n",
    "        self._logger.info(f\"Begin {self.run.__name__}\")\n",
    "        try:\n",
    "            super().run(*args, **kwargs)\n",
    "        except:\n",
    "            traceback.print_exc() \n",
    "            self._logger.info(\"errrrror\")\n",
    "        self._logger.info(f\"End {self.run.__name__}\")\n",
    "\n",
    "    def compose(self):\n",
    "        \"\"\"Creates the app specific operators and chain them up in the processing DAG.\"\"\"\n",
    "        logging.info(f\"Begin {self.compose.__name__}\")\n",
    "\n",
    "        # Create the custom operator(s) as well as SDK built-in operator(s).\n",
    "        study_loader_op = AHIDataLoaderOperator(self.ahi_client)\n",
    "        series_selector_op = DICOMSeriesSelectorOperator(Sample_Rules_Text)\n",
    "        series_to_vol_op = DICOMSeriesToVolumeOperator()\n",
    "\n",
    "        # Create the inference operator that supports MONAI Bundle and automates the inference.\n",
    "        # The IOMapping labels match the input and prediction keys in the pre and post processing.\n",
    "        # The model_name is optional when the app has only one model.\n",
    "        # The bundle_path argument optionally can be set to an accessible bundle file path in the dev\n",
    "        # environment, so when the app is packaged into a MAP, the operator can complete the bundle parsing\n",
    "        # during init to provide the optional packages info, parsed from the bundle, to the packager\n",
    "        # for it to install the packages in the MAP docker image.\n",
    "        # Setting output IOType to DISK only works only for leaf operators, not the case in this example.\n",
    "        #\n",
    "        # Pertinent MONAI Bundle:\n",
    "        #   https://github.com/Project-MONAI/model-zoo/tree/dev/models/spleen_ct_segmentation\n",
    "\n",
    "        config_names = BundleConfigNames(config_names=[\"inference\"])  # Same as the default\n",
    "\n",
    "        bundle_spleen_seg_op = MonaiBundleInferenceOperator(\n",
    "            input_mapping=[IOMapping(\"image\", Image, IOType.IN_MEMORY)],\n",
    "            output_mapping=[IOMapping(\"pred\", Image, IOType.IN_MEMORY)],\n",
    "            bundle_config_names=config_names,\n",
    "        )\n",
    "\n",
    "        # Create DICOM Seg writer providing the required segment description for each segment with\n",
    "        # the actual algorithm and the pertinent organ/tissue. The segment_label, algorithm_name,\n",
    "        # and algorithm_version are of DICOM VR LO type, limited to 64 chars.\n",
    "        # https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_6.2.html\n",
    "        segment_descriptions = [\n",
    "            SegmentDescription(\n",
    "                segment_label=\"Spleen\",\n",
    "                segmented_property_category=codes.SCT.Organ,\n",
    "                segmented_property_type=codes.SCT.Spleen,\n",
    "                algorithm_name=\"volumetric (3D) segmentation of the spleen from CT image\",\n",
    "                algorithm_family=codes.DCM.ArtificialIntelligence,\n",
    "                algorithm_version=\"0.1.0\",\n",
    "            )\n",
    "        ]\n",
    "\n",
    "        custom_tags = {\"SeriesDescription\": \"AI generated Seg, not for clinical use.\"}\n",
    "\n",
    "        dicom_seg_writer = DICOMSegmentationWriterOperator(\n",
    "            segment_descriptions=segment_descriptions, custom_tags=custom_tags\n",
    "        )\n",
    "\n",
    "        # Create the processing pipeline, by specifying the source and destination operators, and\n",
    "        # ensuring the output from the former matches the input of the latter, in both name and type.\n",
    "        self.add_flow(study_loader_op, series_selector_op, {\"dicom_study_list\": \"dicom_study_list\"})\n",
    "        self.add_flow(\n",
    "            series_selector_op, series_to_vol_op, {\"study_selected_series_list\": \"study_selected_series_list\"}\n",
    "        )\n",
    "        self.add_flow(series_to_vol_op, bundle_spleen_seg_op, {\"image\": \"image\"})\n",
    "        # Note below the dicom_seg_writer requires two inputs, each coming from a source operator.\n",
    "        self.add_flow(\n",
    "            series_selector_op, dicom_seg_writer, {\"study_selected_series_list\": \"study_selected_series_list\"}\n",
    "        )\n",
    "        self.add_flow(bundle_spleen_seg_op, dicom_seg_writer, {\"pred\": \"seg_image\"})\n",
    "        # Create the surface mesh STL conversion operator and add it to the app execution flow, if needed, by\n",
    "        # uncommenting the following couple lines.\n",
    "        # stl_conversion_op = STLConversionOperator(output_file=\"stl/spleen.stl\")\n",
    "        # self.add_flow(bundle_spleen_seg_op, stl_conversion_op, {\"pred\": \"image\"})\n",
    "\n",
    "        logging.info(f\"End {self.compose.__name__}\")\n",
    "\n",
    "# This is a sample series selection rule in JSON, simply selecting CT series.\n",
    "# If the study has more than 1 CT series, then all of them will be selected.\n",
    "# Please see more detail in DICOMSeriesSelectorOperator.\n",
    "# For list of string values, e.g. \"ImageType\": [\"PRIMARY\", \"ORIGINAL\"], it is a match if all elements\n",
    "# are all in the multi-value attribute of the DICOM series.\n",
    "\n",
    "Sample_Rules_Text = \"\"\"\n",
    "{\n",
    "    \"selections\": [\n",
    "        {\n",
    "            \"name\": \"CT Series\",\n",
    "            \"conditions\": {\n",
    "                \"Modality\": \"(?i)CT\"\n",
    "            }\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing app locally\n",
    "\n",
    "We can execute the app in the Jupyter notebook. Note that the DICOM files of the CT Abdomen series must be present in the `dcm` and the Torch Script model at `model.ts`. Please use the actual path in your environment.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('inputImageSets.json', 'w') as f:\n",
    "    f.write(json.dumps({\n",
    "        \"datastoreId\": datastoreId, \n",
    "        \"imageSetId\": next(iter(imageSetIds))\n",
    "    }))\n",
    "\n",
    "app = AISpleenSegApp(helper)\n",
    "\n",
    "app.run(input=\"inputImageSets.json\", output=\"output\", model=\"src/model.ts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itk\n",
    "from itkwidgets import view\n",
    "\n",
    "outputimg = itk.imread('output/'+os.listdir('output')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer = view(outputimg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer.set_image_gradient_opacity(0.4)"
   ]
  }
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