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91 lines
3.7 KiB
Python
91 lines
3.7 KiB
Python
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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"""Utils to interact with the Triton Inference Server."""
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import typing
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from urllib.parse import urlparse
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import torch
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class TritonRemoteModel:
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"""
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A wrapper over a model served by the Triton Inference Server.
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It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as
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outputs.
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"""
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def __init__(self, url: str):
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"""
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Keyword Arguments:
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url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000.
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"""
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parsed_url = urlparse(url)
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if parsed_url.scheme == "grpc":
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from tritonclient.grpc import InferenceServerClient, InferInput
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self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client
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model_repository = self.client.get_model_repository_index()
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self.model_name = model_repository.models[0].name
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self.metadata = self.client.get_model_metadata(self.model_name, as_json=True)
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def create_input_placeholders() -> typing.List[InferInput]:
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return [
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InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"]
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]
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else:
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from tritonclient.http import InferenceServerClient, InferInput
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self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client
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model_repository = self.client.get_model_repository_index()
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self.model_name = model_repository[0]["name"]
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self.metadata = self.client.get_model_metadata(self.model_name)
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def create_input_placeholders() -> typing.List[InferInput]:
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return [
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InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"]
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]
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self._create_input_placeholders_fn = create_input_placeholders
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@property
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def runtime(self):
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"""Returns the model runtime."""
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return self.metadata.get("backend", self.metadata.get("platform"))
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def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]:
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"""
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Invokes the model.
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Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of
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the model. kwargs are matched with the model input names.
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"""
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inputs = self._create_inputs(*args, **kwargs)
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response = self.client.infer(model_name=self.model_name, inputs=inputs)
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result = []
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for output in self.metadata["outputs"]:
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tensor = torch.as_tensor(response.as_numpy(output["name"]))
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result.append(tensor)
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return result[0] if len(result) == 1 else result
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def _create_inputs(self, *args, **kwargs):
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"""Creates input tensors from args or kwargs, not both; raises error if none or both are provided."""
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args_len, kwargs_len = len(args), len(kwargs)
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if not args_len and not kwargs_len:
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raise RuntimeError("No inputs provided.")
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if args_len and kwargs_len:
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raise RuntimeError("Cannot specify args and kwargs at the same time")
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placeholders = self._create_input_placeholders_fn()
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if args_len:
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if args_len != len(placeholders):
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raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.")
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for input, value in zip(placeholders, args):
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input.set_data_from_numpy(value.cpu().numpy())
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else:
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for input in placeholders:
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value = kwargs[input.name]
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input.set_data_from_numpy(value.cpu().numpy())
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return placeholders
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