viam.services.mlmodel
Submodules
Package Contents
Classes
Abstract base class for protocol messages. |
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Abstract base class for protocol messages. |
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Abstract base class for protocol messages. |
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MLModel represents a Machine Learning Model service. |
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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 thesuper().__init__()
function.- async infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: Optional[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.
- 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: Optional[float] = 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.
- Returns:
The metadata
- Return type:
- async do_command(command: Mapping[str, viam.utils.ValueTypes], *, timeout: Optional[float] = None, **kwargs) Mapping[str, viam.utils.ValueTypes] [source]
Send/Receive arbitrary commands to the Resource
- Parameters:
command (Mapping[str, ValueTypes]) – The command to execute
- Raises:
NotImplementedError – Raised if the Resource does not support arbitrary commands
- Returns:
Result of the executed command
- Return type:
Mapping[str, ValueTypes]
- classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) typing_extensions.Self
Get the service named
name
from the provided robot.- 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
- Parameters:
name (str) – The name of the Resource
- 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 theOperation
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:
- 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 retun errors when future non-Close methods are called.
- 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 thesuper().__init__()
function.- SUBTYPE: Final
- abstract async infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: Optional[float]) 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.
- 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: Optional[float]) 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.
- Returns:
The metadata
- Return type:
- classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) typing_extensions.Self
Get the service named
name
from the provided robot.- 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: Optional[float] = None, **kwargs) Mapping[str, viam.utils.ValueTypes]
Send/Receive arbitrary commands to the Resource
- Parameters:
command (Mapping[str, ValueTypes]) – The command to execute
- Raises:
NotImplementedError – Raised if the Resource does not support arbitrary commands
- Returns:
Result of the executed command
- Return type:
Mapping[str, ValueTypes]
- classmethod get_resource_name(name: str) viam.proto.common.ResourceName
Get the ResourceName for this Resource with the given name
- Parameters:
name (str) – The name of the Resource
- 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 theOperation
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:
- 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 retun errors when future non-Close methods are called.