`viam.gen.service.mlmodel.v1.mlmodel_pb2`

@generated by mypy-protobuf. Do not edit manually! isort:skip_file

Module Contents

Classes

`LabelType`
`InferRequest`	Abstract base class for protocol messages.
`InferResponse`	Abstract base class for protocol messages.
`MetadataRequest`	Abstract base class for protocol messages.
`MetadataResponse`	Abstract base class for protocol messages.
`Metadata`	Abstract base class for protocol messages.
`TensorInfo`	Abstract base class for protocol messages.
`File`	Abstract base class for protocol messages.
`FlatTensorDataInt8`	Abstract base class for protocol messages.
`FlatTensorDataUInt8`	Abstract base class for protocol messages.
`FlatTensorDataInt16`	Abstract base class for protocol messages.
`FlatTensorDataUInt16`	Abstract base class for protocol messages.
`FlatTensorDataInt32`	Abstract base class for protocol messages.
`FlatTensorDataUInt32`	Abstract base class for protocol messages.
`FlatTensorDataInt64`	Abstract base class for protocol messages.
`FlatTensorDataUInt64`	Abstract base class for protocol messages.
`FlatTensorDataFloat`	Abstract base class for protocol messages.
`FlatTensorDataDouble`	Abstract base class for protocol messages.
`FlatTensor`	Abstract base class for protocol messages.
`FlatTensors`	Abstract base class for protocol messages.

Attributes

`DESCRIPTOR`
`LABEL_TYPE_UNSPECIFIED`
`LABEL_TYPE_TENSOR_VALUE`	the value of the arrays/tensor is the label index
`LABEL_TYPE_TENSOR_AXIS`	the position of the tensor value in the axis is the label index
`global___LabelType`
`global___InferRequest`
`global___InferResponse`
`global___MetadataRequest`
`global___MetadataResponse`
`global___Metadata`
`global___TensorInfo`
`global___File`
`global___FlatTensorDataInt8`
`global___FlatTensorDataUInt8`
`global___FlatTensorDataInt16`
`global___FlatTensorDataUInt16`
`global___FlatTensorDataInt32`
`global___FlatTensorDataUInt32`
`global___FlatTensorDataInt64`
`global___FlatTensorDataUInt64`
`global___FlatTensorDataFloat`
`global___FlatTensorDataDouble`
`global___FlatTensor`
`global___FlatTensors`

viam.gen.service.mlmodel.v1.mlmodel_pb2.DESCRIPTOR: google.protobuf.descriptor.FileDescriptor

class viam.gen.service.mlmodel.v1.mlmodel_pb2.LabelType: Bases: _LabelType

viam.gen.service.mlmodel.v1.mlmodel_pb2.LABEL_TYPE_UNSPECIFIED: LabelType

viam.gen.service.mlmodel.v1.mlmodel_pb2.LABEL_TYPE_TENSOR_VALUE: LabelType: the value of the arrays/tensor is the label index

viam.gen.service.mlmodel.v1.mlmodel_pb2.LABEL_TYPE_TENSOR_AXIS: LabelType: the position of the tensor value in the axis is the label index

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___LabelType

class viam.gen.service.mlmodel.v1.mlmodel_pb2.InferRequest(*, name: str = ..., input_tensors: global___FlatTensors | 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 input_tensors: global___FlatTensors: the input data is provided as set of named flat tensors

property extra: google.protobuf.struct_pb2.Struct: Additional arguments to the method

name: str: name of the model service

HasField(field_name: Literal[extra, b'extra', input_tensors, b'input_tensors']) → 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.

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___InferRequest

class viam.gen.service.mlmodel.v1.mlmodel_pb2.InferResponse(*, output_tensors: global___FlatTensors | 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 output_tensors: global___FlatTensors: the output data is provided as a set of named flat tensors

HasField(field_name: Literal[output_tensors, b'output_tensors']) → 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.

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___InferResponse

class viam.gen.service.mlmodel.v1.mlmodel_pb2.MetadataRequest(*, name: str = ..., 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 extra: google.protobuf.struct_pb2.Struct: Additional arguments to the method

name: str: name of the model service

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.

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___MetadataRequest

class viam.gen.service.mlmodel.v1.mlmodel_pb2.MetadataResponse(*, metadata: global___Metadata | 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 metadata: global___Metadata: this is the metadata associated with the ML model

HasField(field_name: Literal[metadata, b'metadata']) → 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.

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___MetadataResponse

class viam.gen.service.mlmodel.v1.mlmodel_pb2.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

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___Metadata

class viam.gen.service.mlmodel.v1.mlmodel_pb2.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.

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___TensorInfo

class viam.gen.service.mlmodel.v1.mlmodel_pb2.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

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___File

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataInt8(*, data: bytes = ...)

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.

data: bytes

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataInt8

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataUInt8(*, data: bytes = ...)

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.

data: bytes

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataUInt8

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataInt16(*, data: collections.abc.Iterable[int] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]: packs two 16-bit numbers per entry - explicitly little-endian so big-endian producers/consumers must compensate

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataInt16

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataUInt16(*, data: collections.abc.Iterable[int] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]: packs two 16-bit numbers per entry - explicitly little-endian so big-endian producers/consumers must compensate

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataUInt16

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataInt32(*, data: collections.abc.Iterable[int] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataInt32

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataUInt32(*, data: collections.abc.Iterable[int] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataUInt32

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataInt64(*, data: collections.abc.Iterable[int] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataInt64

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataUInt64(*, data: collections.abc.Iterable[int] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int]

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataUInt64

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataFloat(*, data: collections.abc.Iterable[float] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[float]

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataFloat

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensorDataDouble(*, data: collections.abc.Iterable[float] | 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 data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[float]

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensorDataDouble

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensor(*, shape: collections.abc.Iterable[int] | None = ..., int8_tensor: global___FlatTensorDataInt8 | None = ..., uint8_tensor: global___FlatTensorDataUInt8 | None = ..., int16_tensor: global___FlatTensorDataInt16 | None = ..., uint16_tensor: global___FlatTensorDataUInt16 | None = ..., int32_tensor: global___FlatTensorDataInt32 | None = ..., uint32_tensor: global___FlatTensorDataUInt32 | None = ..., int64_tensor: global___FlatTensorDataInt64 | None = ..., uint64_tensor: global___FlatTensorDataUInt64 | None = ..., float_tensor: global___FlatTensorDataFloat | None = ..., double_tensor: global___FlatTensorDataDouble | 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]: the shape of the provided tensor as a list of integer extents

property int8_tensor: global___FlatTensorDataInt8

property uint8_tensor: global___FlatTensorDataUInt8

property int16_tensor: global___FlatTensorDataInt16

property uint16_tensor: global___FlatTensorDataUInt16

property int32_tensor: global___FlatTensorDataInt32

property uint32_tensor: global___FlatTensorDataUInt32

property int64_tensor: global___FlatTensorDataInt64

property uint64_tensor: global___FlatTensorDataUInt64

property float_tensor: global___FlatTensorDataFloat

property double_tensor: global___FlatTensorDataDouble

HasField(field_name: Literal[double_tensor, b'double_tensor', float_tensor, b'float_tensor', int16_tensor, b'int16_tensor', int32_tensor, b'int32_tensor', int64_tensor, b'int64_tensor', int8_tensor, b'int8_tensor', tensor, b'tensor', uint16_tensor, b'uint16_tensor', uint32_tensor, b'uint32_tensor', uint64_tensor, b'uint64_tensor', uint8_tensor, b'uint8_tensor']) → 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.

WhichOneof(oneof_group: Literal[tensor, b'tensor']) → Literal[int8_tensor, uint8_tensor, int16_tensor, uint16_tensor, int32_tensor, uint32_tensor, int64_tensor, uint64_tensor, float_tensor, double_tensor] | None

Returns the name of the field that is set inside a oneof group.

If no field is set, returns None.

Parameters:: oneof_group (str) – the name of the oneof group to check.
Returns:: The name of the group that is set, or None.
Return type:: str or None
Raises:: ValueError – no group with the given name exists

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensor

class viam.gen.service.mlmodel.v1.mlmodel_pb2.FlatTensors(*, tensors: collections.abc.Mapping[str, global___FlatTensor] | 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.

class TensorsEntry(*, key: str = ..., value: global___FlatTensor | 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 value: global___FlatTensor

key: str

HasField(field_name: Literal[value, b'value']) → 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.

property tensors: google.protobuf.internal.containers.MessageMap[str, global___FlatTensor]: A name-indexed collection of flat tensor objects

viam.gen.service.mlmodel.v1.mlmodel_pb2.global___FlatTensors

viam.gen.service.mlmodel.v1.mlmodel_pb2

Module Contents

Classes

Attributes

`viam.gen.service.mlmodel.v1.mlmodel_pb2`