ultralytics/ultralytics/utils/triton.py

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

from typing import List
from urllib.parse import urlsplit

import numpy as np


class TritonRemoteModel:
    """
    Client for interacting with a remote Triton Inference Server model.

    This class provides a convenient interface for sending inference requests to a Triton Inference Server
    and processing the responses.

    Attributes:
        endpoint (str): The name of the model on the Triton server.
        url (str): The URL of the Triton server.
        triton_client: The Triton client (either HTTP or gRPC).
        InferInput: The input class for the Triton client.
        InferRequestedOutput: The output request class for the Triton client.
        input_formats (List[str]): The data types of the model inputs.
        np_input_formats (List[type]): The numpy data types of the model inputs.
        input_names (List[str]): The names of the model inputs.
        output_names (List[str]): The names of the model outputs.
        metadata: The metadata associated with the model.

    Methods:
        __call__: Call the model with the given inputs and return the outputs.

    Examples:
        Initialize a Triton client with HTTP
        >>> model = TritonRemoteModel(url="localhost:8000", endpoint="yolov8", scheme="http")
        Make inference with numpy arrays
        >>> outputs = model(np.random.rand(1, 3, 640, 640).astype(np.float32))
    """

    def __init__(self, url: str, endpoint: str = "", scheme: str = ""):
        """
        Initialize the TritonRemoteModel for interacting with a remote Triton Inference Server.

        Arguments may be provided individually or parsed from a collective 'url' argument of the form
        <scheme>://<netloc>/<endpoint>/<task_name>

        Args:
            url (str): The URL of the Triton server.
            endpoint (str): The name of the model on the Triton server.
            scheme (str): The communication scheme ('http' or 'grpc').

        Examples:
            >>> model = TritonRemoteModel(url="localhost:8000", endpoint="yolov8", scheme="http")
            >>> model = TritonRemoteModel(url="http://localhost:8000/yolov8")
        """
        if not endpoint and not scheme:  # Parse all args from URL string
            splits = urlsplit(url)
            endpoint = splits.path.strip("/").split("/")[0]
            scheme = splits.scheme
            url = splits.netloc

        self.endpoint = endpoint
        self.url = url

        # Choose the Triton client based on the communication scheme
        if scheme == "http":
            import tritonclient.http as client  # noqa

            self.triton_client = client.InferenceServerClient(url=self.url, verbose=False, ssl=False)
            config = self.triton_client.get_model_config(endpoint)
        else:
            import tritonclient.grpc as client  # noqa

            self.triton_client = client.InferenceServerClient(url=self.url, verbose=False, ssl=False)
            config = self.triton_client.get_model_config(endpoint, as_json=True)["config"]

        # Sort output names alphabetically, i.e. 'output0', 'output1', etc.
        config["output"] = sorted(config["output"], key=lambda x: x.get("name"))

        # Define model attributes
        type_map = {"TYPE_FP32": np.float32, "TYPE_FP16": np.float16, "TYPE_UINT8": np.uint8}
        self.InferRequestedOutput = client.InferRequestedOutput
        self.InferInput = client.InferInput
        self.input_formats = [x["data_type"] for x in config["input"]]
        self.np_input_formats = [type_map[x] for x in self.input_formats]
        self.input_names = [x["name"] for x in config["input"]]
        self.output_names = [x["name"] for x in config["output"]]
        self.metadata = eval(config.get("parameters", {}).get("metadata", {}).get("string_value", "None"))

    def __call__(self, *inputs: np.ndarray) -> List[np.ndarray]:
        """
        Call the model with the given inputs.

        Args:
            *inputs (np.ndarray): Input data to the model. Each array should match the expected shape and type
                for the corresponding model input.

        Returns:
            (List[np.ndarray]): Model outputs with the same dtype as the input. Each element in the list
                corresponds to one of the model's output tensors.

        Examples:
            >>> model = TritonRemoteModel(url="localhost:8000", endpoint="yolov8", scheme="http")
            >>> outputs = model(np.random.rand(1, 3, 640, 640).astype(np.float32))
        """
        infer_inputs = []
        input_format = inputs[0].dtype
        for i, x in enumerate(inputs):
            if x.dtype != self.np_input_formats[i]:
                x = x.astype(self.np_input_formats[i])
            infer_input = self.InferInput(self.input_names[i], [*x.shape], self.input_formats[i].replace("TYPE_", ""))
            infer_input.set_data_from_numpy(x)
            infer_inputs.append(infer_input)

        infer_outputs = [self.InferRequestedOutput(output_name) for output_name in self.output_names]
        outputs = self.triton_client.infer(model_name=self.endpoint, inputs=infer_inputs, outputs=infer_outputs)

        return [outputs.as_numpy(output_name).astype(input_format) for output_name in self.output_names]