`viam.services.mlmodel`

Submodules

Package Contents

Classes

`File`	Abstract base class for protocol messages.
`LabelType`
`Metadata`	Abstract base class for protocol messages.
`TensorInfo`	Abstract base class for protocol messages.
`MLModelClient`	MLModel represents a Machine Learning Model service.
`MLModel`	MLModel represents a Machine Learning Model service.

class viam.services.mlmodel.File(*, name: str = ..., description: str = ..., label_type: global___LabelType = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

name: str: name of the file, with file extension

description: str: description of what the file contains

label_type: global___LabelType: How to associate the arrays/tensors to the labels in the file

class viam.services.mlmodel.LabelType: Bases: _LabelType

class viam.services.mlmodel.Metadata(*, name: str = ..., type: str = ..., description: str = ..., input_info: collections.abc.Iterable[global___TensorInfo] | None = ..., output_info: collections.abc.Iterable[global___TensorInfo] | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

property input_info: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorInfo]: the necessary input arrays/tensors for an inference, order matters

property output_info: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorInfo]: the output arrays/tensors of the model, order matters

name: str: name of the model

type: str: type of model e.g. object_detector, text_classifier

description: str: description of the model

class viam.services.mlmodel.TensorInfo(*, name: str = ..., description: str = ..., data_type: str = ..., shape: collections.abc.Iterable[int] | None = ..., associated_files: collections.abc.Iterable[global___File] | None = ..., extra: google.protobuf.struct_pb2.Struct | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

property shape: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]: shape of the array/tensor (-1 for unknown)

property associated_files: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___File]: files associated with the array/tensor, like for category labels

property extra: google.protobuf.struct_pb2.Struct: anything else you want to say

name: str: name of the data in the array/tensor

description: str: description of the data in the array/tensor

data_type: str: data type of the array/tensor, e.g. float32, float64, uint8

HasField(field_name: Literal[extra, b'extra']) → bool

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:: field_name (str) – The name of the field to check for presence.
Returns:: Whether a value has been set for the named field.
Return type:: bool
Raises:: ValueError – if the field_name is not a member of this message.

class viam.services.mlmodel.MLModelClient(name: str, channel: grpclib.client.Channel)[source]

Bases: viam.services.mlmodel.mlmodel.MLModel, viam.resource.rpc_client_base.ReconfigurableResourceRPCClientBase

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.

async infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: float | None = None) → Dict[str, numpy.typing.NDArray][source]

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)

Parameters:: input_tensors (Dict[str, NDArray]) – A dictionary of input flat tensors as specified in the metadata
Returns:: A dictionary of output flat tensors as specified in the metadata
Return type:: Dict[str, NDArray]

async metadata(*, timeout: float | None = None) → viam.services.mlmodel.mlmodel.Metadata[source]

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:: The metadata
Return type:: Metadata

async do_command(command: Mapping[str, viam.utils.ValueTypes], *, timeout: float | None = None, **kwargs) → Mapping[str, viam.utils.ValueTypes][source]

Send/receive arbitrary commands.

motion = MotionClient.from_robot(robot, "builtin")

my_command = {
  "cmnd": "dosomething",
  "someparameter": 52
}

# Can be used with any resource, using the motion service as an example
await motion.do_command(command=my_command)

Parameters:: command (Dict[str, ValueTypes]) – The command to execute
Returns:: Result of the executed command
Return type:: Dict[str, ValueTypes]

classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) → typing_extensions.Self

Get the service named name from the provided robot.

async def connect() -> ViamClient:
    # Replace "<API-KEY>" (including brackets) with your API key and "<API-KEY-ID>" with your API key ID
    dial_options = DialOptions.with_api_key("<API-KEY>", "<API-KEY-ID>")
    return await ViamClient.create_from_dial_options(dial_options)

async def main():
    robot = await connect()

    # Can be used with any resource, using the motion service as an example
    motion = MotionClient.from_robot(robot=robot, name="builtin")

    robot.close()

Parameters:

robot (RobotClient) – The robot
name (str) – The name of the service

Returns:

The service, if it exists on the robot

Return type:

Self

classmethod get_resource_name(name: str) → viam.proto.common.ResourceName

Get the ResourceName for this Resource with the given name

# Can be used with any resource, using an arm as an example
my_arm_name = my_arm.get_resource_name("my_arm")

Parameters:: name (str) – The name of the Resource
Returns:: The ResourceName of this Resource
Return type:: ResourceName

get_operation(kwargs: Mapping[str, Any]) → viam.operations.Operation

Get the Operation associated with the currently running function.

When writing custom resources, you should get the Operation by calling this function and check to see if it’s cancelled. If the Operation is cancelled, then you can perform any necessary (terminating long running tasks, cleaning up connections, etc. ).

Parameters:: kwargs (Mapping[str, Any]) – The kwargs object containing the operation
Returns:: The operation associated with this function
Return type:: viam.operations.Operation

async close()

Safely shut down the resource and prevent further use.

Close must be idempotent. Later configuration may allow a resource to be “open” again. If a resource does not want or need a close function, it is assumed that the resource does not need to return errors when future non-Close methods are called.

await component.close()

class viam.services.mlmodel.MLModel(name: str)[source]

Bases: viam.services.service_base.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.

SUBTYPE: Final

abstract async infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: float | None) → Dict[str, numpy.typing.NDArray][source]

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)

Parameters:: input_tensors (Dict[str, NDArray]) – A dictionary of input flat tensors as specified in the metadata
Returns:: A dictionary of output flat tensors as specified in the metadata
Return type:: Dict[str, NDArray]

abstract async metadata(*, timeout: float | None) → viam.proto.service.mlmodel.Metadata[source]

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:: The metadata
Return type:: Metadata

classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) → typing_extensions.Self

Get the service named name from the provided robot.

async def connect() -> ViamClient:
    # Replace "<API-KEY>" (including brackets) with your API key and "<API-KEY-ID>" with your API key ID
    dial_options = DialOptions.with_api_key("<API-KEY>", "<API-KEY-ID>")
    return await ViamClient.create_from_dial_options(dial_options)

async def main():
    robot = await connect()

    # Can be used with any resource, using the motion service as an example
    motion = MotionClient.from_robot(robot=robot, name="builtin")

    robot.close()

Parameters:

robot (RobotClient) – The robot
name (str) – The name of the service

Returns:

The service, if it exists on the robot

Return type:

Self

abstract async do_command(command: Mapping[str, viam.utils.ValueTypes], *, timeout: float | None = None, **kwargs) → Mapping[str, viam.utils.ValueTypes]

Send/receive arbitrary commands.

motion = MotionClient.from_robot(robot, "builtin")

my_command = {
  "cmnd": "dosomething",
  "someparameter": 52
}

# Can be used with any resource, using the motion service as an example
await motion.do_command(command=my_command)

Parameters:: command (Dict[str, ValueTypes]) – The command to execute
Returns:: Result of the executed command
Return type:: Dict[str, ValueTypes]

classmethod get_resource_name(name: str) → viam.proto.common.ResourceName

Get the ResourceName for this Resource with the given name

# Can be used with any resource, using an arm as an example
my_arm_name = my_arm.get_resource_name("my_arm")

Parameters:: name (str) – The name of the Resource
Returns:: The ResourceName of this Resource
Return type:: ResourceName

get_operation(kwargs: Mapping[str, Any]) → viam.operations.Operation

Get the Operation associated with the currently running function.

When writing custom resources, you should get the Operation by calling this function and check to see if it’s cancelled. If the Operation is cancelled, then you can perform any necessary (terminating long running tasks, cleaning up connections, etc. ).

Parameters:: kwargs (Mapping[str, Any]) – The kwargs object containing the operation
Returns:: The operation associated with this function
Return type:: viam.operations.Operation

async close()

Safely shut down the resource and prevent further use.

Close must be idempotent. Later configuration may allow a resource to be “open” again. If a resource does not want or need a close function, it is assumed that the resource does not need to return errors when future non-Close methods are called.

await component.close()

viam.services.mlmodel

Submodules

Package Contents

Classes

`viam.services.mlmodel`