Source code for

import abc
from typing import Dict, Final, Optional

from numpy.typing import NDArray

from viam.proto.service.mlmodel import Metadata
from viam.resource.types import RESOURCE_NAMESPACE_RDK, RESOURCE_TYPE_SERVICE, Subtype

from ..service_base import ServiceBase

[docs]class MLModel(ServiceBase): """ MLModel represents a Machine Learning Model service. This acts as an abstract base class for any drivers representing specific arm implementations. This cannot be used on its own. If the ``__init__()`` function is overridden, it must call the ``super().__init__()`` function. For more information, see `ML model service <>`_. """ SUBTYPE: Final = Subtype( # pyright: ignore [reportIncompatibleVariableOverride] RESOURCE_NAMESPACE_RDK, RESOURCE_TYPE_SERVICE, "mlmodel" )
[docs] @abc.abstractmethod async def infer(self, input_tensors: Dict[str, NDArray], *, timeout: Optional[float]) -> Dict[str, NDArray]: """Take an already ordered input tensor as an array, make an inference on the model, and return an output tensor map. :: import numpy as np my_mlmodel = MLModelClient.from_robot(robot=robot, name="my_mlmodel_service") nd_array = np.array([1, 2, 3], dtype=np.float64) input_tensors = {"0": nd_array} output_tensors = await my_mlmodel.infer(input_tensors) Args: input_tensors (Dict[str, NDArray]): A dictionary of input flat tensors as specified in the metadata Returns: Dict[str, NDArray]: A dictionary of output flat tensors as specified in the metadata For more information, see `ML model service <>`_. """ ...
[docs] @abc.abstractmethod async def metadata(self, *, timeout: Optional[float]) -> Metadata: """Get the metadata (such as name, type, expected tensor/array shape, inputs, and outputs) associated with the ML model. :: my_mlmodel = MLModelClient.from_robot(robot=robot, name="my_mlmodel_service") metadata = await my_mlmodel.metadata() Returns: Metadata: The metadata For more information, see `ML model service <>`_. """ ...