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ultralytics/utils/triton.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+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. Supports both HTTP and gRPC communication protocols.
+
+    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, optional): The name of the model on the Triton server.
+            scheme (str, optional): 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("/", 1)[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 and return inference results.
+
+        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]