Documentation
¶
Overview ¶
Package mlmodel defines the client and server for a service that can take in a map of input tensors/arrays, pass them through an inference engine, and then return a map output tensors/arrays. For more information, see the ML model service docs.
Index ¶
Constants ¶
const (
// LabelTypeUnspecified means the label type is not known.
LabelTypeUnspecified = LabelType("UNSPECIFIED")
// LabelTypeTensorValue means the labels are assigned by the actual value in the tensor
// e.g. for 4 results and 3 categories : [0, 1, 2, 1].
LabelTypeTensorValue = LabelType("TENSOR_VALUE")
// LabelTypeTensorAxis means labels are assigned by the position within the tensor axis
// e.g. for 4 results and 3 categories : [[.8, .1, .1], [.2, .7, .1], [.1, .1, .8],[.05, .9, .05]].
LabelTypeTensorAxis = LabelType("TENSOR_AXIS")
)
const SubtypeName = "mlmodel"
SubtypeName is the name of the type of service.
Variables ¶
var API = resource.APINamespaceRDK.WithServiceType(SubtypeName)
API is a variable that identifies the ML model service resource API.
Functions ¶
func Named ¶
func Named(name string) resource.Name
Named is a helper for getting the named ML model service's typed resource name.
func NewRPCServiceServer ¶ added in v0.2.36
func NewRPCServiceServer(coll resource.APIResourceCollection[Service]) interface{}
NewRPCServiceServer constructs a ML Model gRPC service server. It is intentionally untyped to prevent use outside of tests.
func TensorsToProto ¶ added in v0.8.0
func TensorsToProto(ts ml.Tensors) (*servicepb.FlatTensors, error)
TensorsToProto turns the ml.Tensors map into a protobuf message of FlatTensors.
Types ¶
type File ¶
type File struct {
Name string // e.g. category_labels.txt
Description string
LabelType LabelType // TENSOR_VALUE, or TENSOR_AXIS
}
File contains information about how to interpret the numbers within the tensor/array. The label type describes how to read the tensor in order to successfully label the numbers.
type LabelType ¶
type LabelType string
LabelType describes how labels from the file are assigned to the tensors. TENSOR_VALUE means that labels are the actual value in the tensor. TENSOR_AXIS means that labels are positional within the tensor axis.
type MLMetadata ¶
type MLMetadata struct {
ModelName string
ModelType string // e.g. object_detector, text_classifier
ModelDescription string
Inputs []TensorInfo
Outputs []TensorInfo
}
MLMetadata contains the metadata of the model file, such as the name of the model, what kind of model it is, and the expected tensor/array shape and types of the inputs and outputs of the model.
type Service ¶
type Service interface {
resource.Resource
// Infer returns an output tensor map after running an input tensor map through an interface model.
Infer(ctx context.Context, tensors ml.Tensors) (ml.Tensors, error)
// Metadata returns the metadata: name, data type, expected tensor/array shape, inputs, and outputs associated with the ML model.
Metadata(ctx context.Context) (MLMetadata, error)
}
Service defines the ML Model interface, which takes a map of inputs, runs it through an inference engine, and creates a map of outputs. Metadata is necessary in order to build the struct that will decode that map[string]interface{} correctly. For more information, see the ML model service docs.
Infer example:
import (
"go.viam.com/rdk/ml"
"gorgonia.org/tensor"
)
myMLModel, err := mlmodel.FromRobot(machine, "my_mlmodel")
input_tensors := ml.Tensors{
"image": tensor.New(
tensor.Of(tensor.Uint8),
tensor.WithShape(1, 384, 384, 3),
tensor.WithBacking(make([]uint8, 1*384*384*3)),
),
}
output_tensors, err := myMLModel.Infer(context.Background(), input_tensors)
For more information, see the Infer method docs.
Metadata example:
myMLModel, err := mlmodel.FromRobot(machine, "my_mlmodel")
metadata, err := myMLModel.Metadata(context.Background())
For more information, see the Metadata method docs.
func FromRobot ¶
func FromRobot(r robot.Robot, name string) (Service, error)
FromRobot is a helper for getting the named ML model service from the given Robot.
func NewClientFromConn ¶
func NewClientFromConn(
ctx context.Context,
conn rpc.ClientConn,
remoteName string,
name resource.Name,
logger logging.Logger,
) (Service, error)
NewClientFromConn constructs a new Client from connection passed in.
type TensorInfo ¶
type TensorInfo struct {
Name string // e.g. bounding_boxes
Description string
DataType string // e.g. uint8, float32, int
Shape []int // number of dimensions in the array
AssociatedFiles []File
Extra map[string]interface{}
}
TensorInfo contains all the information necessary to build a struct from the input and output maps. it describes the name of the output field, what data type it has, and how many dimensions the array/tensor will have. AssociatedFiles points to where more information is located, e.g. in case the ints within the array/tensor need to be converted into a string.
Source Files
Directories