model.py

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import json
import logging
import numpy as np
import os
import pathlib
from tempfile import NamedTemporaryFile
import torch
import torch.backends.cudnn as cudnn

from monai.apps.utils import download_and_extract
from monai.inferers.inferer import SimpleInferer
from monai.transforms import Compose
from monai.transforms import (
    Activations,
    EnsureChannelFirst,
    AsDiscrete,
    CropForeground,
    LoadImage,
    Lambda,
    ScaleIntensityRange,
    ToNumpy,
    ToTensor,
    Resize,
)

import triton_python_backend_utils as pb_utils


logger = logging.getLogger(__name__)
gdrive_url = "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/covid19_model.tar.gz"
model_filename = "covid19_model.tar.gz"
md5_check = "571046a25659515bf7abee4266f14435"


class TritonPythonModel:
    """
    Your Python model must use the same class name. Every Python model
    that is created must have "TritonPythonModel" as the class name.
    """

    def initialize(self, args):
        """
        `initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
        the model to intialize any state associated with this model.
        """

        # Pull model from google drive
        extract_dir = "/models/monai_covid/1"
        tar_save_path = os.path.join(extract_dir, model_filename)
        download_and_extract(gdrive_url, tar_save_path, output_dir=extract_dir, hash_val=md5_check, hash_type="md5")
        # load model configuration
        self.model_config = json.loads(args["model_config"])

        # create inferer engine and load PyTorch model
        inference_device_kind = args.get("model_instance_kind", None)
        logger.info(f"Inference device: {inference_device_kind}")

        self.inference_device = torch.device("cpu")
        if inference_device_kind is None or inference_device_kind == "CPU":
            self.inference_device = torch.device("cpu")
        elif inference_device_kind == "GPU":
            inference_device_id = args.get("model_instance_device_id", "0")
            logger.info(f"Inference device id: {inference_device_id}")

            if torch.cuda.is_available():
                self.inference_device = torch.device(f"cuda:{inference_device_id}")
                cudnn.enabled = True
            else:
                logger.error(f"No CUDA device detected. Using device: {inference_device_kind}")

        # create pre-transforms
        self.pre_transforms = Compose(
            [
                LoadImage(reader="NibabelReader", image_only=True, dtype=np.float32),
                EnsureChannelFirst(channel_dim="no_channel"),
                ScaleIntensityRange(a_min=-1000, a_max=500, b_min=0.0, b_max=1.0, clip=True),
                CropForeground(margin=5, allow_smaller=True),
                Resize([192, 192, 64], mode="area"),
                EnsureChannelFirst(channel_dim="no_channel"),
                ToTensor(),
                Lambda(func=lambda x: x.to(device=self.inference_device)),
            ]
        )

        # create post-transforms
        self.post_transforms = Compose(
            [
                Lambda(func=lambda x: x.to(device="cpu")),
                Activations(sigmoid=True),
                ToNumpy(),
                AsDiscrete(threshold=True),
            ]
        )

        self.inferer = SimpleInferer()

        self.model = torch.jit.load(
            f"{pathlib.Path(os.path.realpath(__file__)).parent}{os.path.sep}covid19_model.ts",
            map_location=self.inference_device,
        )

    def execute(self, requests):
        """
        `execute` must be implemented in every Python model. `execute`
        function receives a list of pb_utils.InferenceRequest as the only
        argument. This function is called when an inference is requested
        for this model. Depending on the batching configuration (e.g. Dynamic
        Batching) used, `requests` may contain multiple requests. Every
        Python model, must create one pb_utils.InferenceResponse for every
        pb_utils.InferenceRequest in `requests`. If there is an error, you can
        set the error argument when creating a pb_utils.InferenceResponse.
        """

        responses = []

        for request in requests:
            # get the input by name (as configured in config.pbtxt)
            input_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")

            tmpFile = NamedTemporaryFile(delete=False, suffix=".nii.gz")
            tmpFile.seek(0)
            tmpFile.write(input_0.as_numpy().astype(np.bytes_).tobytes())
            tmpFile.close()

            transform_output = self.pre_transforms(tmpFile.name)

            with torch.no_grad():
                inference_output = self.inferer(transform_output, self.model)

            classification_output = self.post_transforms(inference_output)
            class_data = np.array([bytes("COVID Negative", encoding="utf-8")], dtype=np.bytes_)

            if classification_output[0] > 0:
                class_data = np.array([bytes("COVID Positive", encoding="utf-8")], dtype=np.bytes_)

            output0_tensor = pb_utils.Tensor(
                "OUTPUT0",
                class_data,
            )
            inference_response = pb_utils.InferenceResponse(
                output_tensors=[output0_tensor],
            )
            responses.append(inference_response)

        return responses

    def finalize(self):
        """
        `finalize` is called only once when the model is being unloaded.
        Implementing `finalize` function is optional. This function allows
        the model to perform any necessary clean ups before exit.
        """
        pass