examples/preprocessing/model.py - main - rnekrasov/python_backend

examples/preprocessing/model.py
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# Copyright 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
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#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above copyright
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#  * Neither the name of NVIDIA CORPORATION nor the names of its
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#
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

import io
import json

import numpy as np
import torchvision.transforms as transforms

# triton_python_backend_utils is available in every Triton Python model. You
# need to use this module to create inference requests and responses. It also
# contains some utility functions for extracting information from model_config
# and converting Triton input/output types to numpy types.
import triton_python_backend_utils as pb_utils
from PIL import Image


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 initialize any state associated with this model.

        Parameters
        ----------
        args : dict
          Both keys and values are strings. The dictionary keys and values are:
          * model_config: A JSON string containing the model configuration
          * model_instance_kind: A string containing model instance kind
          * model_instance_device_id: A string containing model instance device ID
          * model_repository: Model repository path
          * model_version: Model version
          * model_name: Model name
        """

        # You must parse model_config. JSON string is not parsed here
        self.model_config = model_config = json.loads(args["model_config"])

        # Get OUTPUT0 configuration
        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT_0")

        # Convert Triton types to numpy types
        self.output0_dtype = pb_utils.triton_string_to_numpy(
            output0_config["data_type"]
        )

    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 request is made
        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

        Parameters
        ----------
        requests : list
          A list of pb_utils.InferenceRequest

        Returns
        -------
        list
          A list of pb_utils.InferenceResponse. The length of this list must
          be the same as `requests`
        """

        output0_dtype = self.output0_dtype

        responses = []

        # Every Python backend must iterate over everyone of the requests
        # and create a pb_utils.InferenceResponse for each of them.
        for request in requests:
            # Get INPUT0
            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT_0")

            normalize = transforms.Normalize(
                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
            )

            loader = transforms.Compose(
                [
                    transforms.Resize([224, 224]),
                    transforms.CenterCrop(224),
                    transforms.ToTensor(),
                    normalize,
                ]
            )

            def image_loader(image_name):
                image = loader(image_name)
                # expand the dimension to nchw
                image = image.unsqueeze(0)
                return image

            img = in_0.as_numpy()

            image = Image.open(io.BytesIO(img.tobytes()))
            img_out = image_loader(image)
            img_out = np.array(img_out)

            out_tensor_0 = pb_utils.Tensor("OUTPUT_0", img_out.astype(output0_dtype))

            # Create InferenceResponse. You can set an error here in case
            # there was a problem with handling this inference request.
            # Below is an example of how you can set errors in inference
            # response:
            #
            # pb_utils.InferenceResponse(
            #    output_tensors=..., TritonError("An error occurred"))
            inference_response = pb_utils.InferenceResponse(
                output_tensors=[out_tensor_0]
            )
            responses.append(inference_response)

        # You should return a list of pb_utils.InferenceResponse. Length
        # of this list must match the length of `requests` list.
        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.
        """
        print("Cleaning up...")