/
data_labeling_job.pb.go
executable file
·930 lines (842 loc) · 45.4 KB
/
data_labeling_job.pb.go
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// Copyright 2023 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.32.0
// protoc v4.25.2
// source: google/cloud/aiplatform/v1beta1/data_labeling_job.proto
package aiplatformpb
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
status "google.golang.org/genproto/googleapis/rpc/status"
money "google.golang.org/genproto/googleapis/type/money"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
structpb "google.golang.org/protobuf/types/known/structpb"
timestamppb "google.golang.org/protobuf/types/known/timestamppb"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Sample strategy decides which subset of DataItems should be selected for
// human labeling in every batch.
type SampleConfig_SampleStrategy int32
const (
// Default will be treated as UNCERTAINTY.
SampleConfig_SAMPLE_STRATEGY_UNSPECIFIED SampleConfig_SampleStrategy = 0
// Sample the most uncertain data to label.
SampleConfig_UNCERTAINTY SampleConfig_SampleStrategy = 1
)
// Enum value maps for SampleConfig_SampleStrategy.
var (
SampleConfig_SampleStrategy_name = map[int32]string{
0: "SAMPLE_STRATEGY_UNSPECIFIED",
1: "UNCERTAINTY",
}
SampleConfig_SampleStrategy_value = map[string]int32{
"SAMPLE_STRATEGY_UNSPECIFIED": 0,
"UNCERTAINTY": 1,
}
)
func (x SampleConfig_SampleStrategy) Enum() *SampleConfig_SampleStrategy {
p := new(SampleConfig_SampleStrategy)
*p = x
return p
}
func (x SampleConfig_SampleStrategy) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (SampleConfig_SampleStrategy) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_enumTypes[0].Descriptor()
}
func (SampleConfig_SampleStrategy) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_enumTypes[0]
}
func (x SampleConfig_SampleStrategy) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use SampleConfig_SampleStrategy.Descriptor instead.
func (SampleConfig_SampleStrategy) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescGZIP(), []int{2, 0}
}
// DataLabelingJob is used to trigger a human labeling job on unlabeled data
// from the following Dataset:
type DataLabelingJob struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. Resource name of the DataLabelingJob.
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Required. The user-defined name of the DataLabelingJob.
// The name can be up to 128 characters long and can consist of any UTF-8
// characters.
// Display name of a DataLabelingJob.
DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
// Required. Dataset resource names. Right now we only support labeling from a
// single Dataset. Format:
// `projects/{project}/locations/{location}/datasets/{dataset}`
Datasets []string `protobuf:"bytes,3,rep,name=datasets,proto3" json:"datasets,omitempty"`
// Labels to assign to annotations generated by this DataLabelingJob.
//
// Label keys and values can be no longer than 64 characters
// (Unicode codepoints), can only contain lowercase letters, numeric
// characters, underscores and dashes. International characters are allowed.
// See https://goo.gl/xmQnxf for more information and examples of labels.
// System reserved label keys are prefixed with "aiplatform.googleapis.com/"
// and are immutable.
AnnotationLabels map[string]string `protobuf:"bytes,12,rep,name=annotation_labels,json=annotationLabels,proto3" json:"annotation_labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Required. Number of labelers to work on each DataItem.
LabelerCount int32 `protobuf:"varint,4,opt,name=labeler_count,json=labelerCount,proto3" json:"labeler_count,omitempty"`
// Required. The Google Cloud Storage location of the instruction pdf. This
// pdf is shared with labelers, and provides detailed description on how to
// label DataItems in Datasets.
InstructionUri string `protobuf:"bytes,5,opt,name=instruction_uri,json=instructionUri,proto3" json:"instruction_uri,omitempty"`
// Required. Points to a YAML file stored on Google Cloud Storage describing
// the config for a specific type of DataLabelingJob. The schema files that
// can be used here are found in the
// https://storage.googleapis.com/google-cloud-aiplatform bucket in the
// /schema/datalabelingjob/inputs/ folder.
InputsSchemaUri string `protobuf:"bytes,6,opt,name=inputs_schema_uri,json=inputsSchemaUri,proto3" json:"inputs_schema_uri,omitempty"`
// Required. Input config parameters for the DataLabelingJob.
Inputs *structpb.Value `protobuf:"bytes,7,opt,name=inputs,proto3" json:"inputs,omitempty"`
// Output only. The detailed state of the job.
State JobState `protobuf:"varint,8,opt,name=state,proto3,enum=google.cloud.aiplatform.v1beta1.JobState" json:"state,omitempty"`
// Output only. Current labeling job progress percentage scaled in interval
// [0, 100], indicating the percentage of DataItems that has been finished.
LabelingProgress int32 `protobuf:"varint,13,opt,name=labeling_progress,json=labelingProgress,proto3" json:"labeling_progress,omitempty"`
// Output only. Estimated cost(in US dollars) that the DataLabelingJob has
// incurred to date.
CurrentSpend *money.Money `protobuf:"bytes,14,opt,name=current_spend,json=currentSpend,proto3" json:"current_spend,omitempty"`
// Output only. Timestamp when this DataLabelingJob was created.
CreateTime *timestamppb.Timestamp `protobuf:"bytes,9,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
// Output only. Timestamp when this DataLabelingJob was updated most recently.
UpdateTime *timestamppb.Timestamp `protobuf:"bytes,10,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
// Output only. DataLabelingJob errors. It is only populated when job's state
// is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
Error *status.Status `protobuf:"bytes,22,opt,name=error,proto3" json:"error,omitempty"`
// The labels with user-defined metadata to organize your DataLabelingJobs.
//
// Label keys and values can be no longer than 64 characters
// (Unicode codepoints), can only contain lowercase letters, numeric
// characters, underscores and dashes. International characters are allowed.
//
// See https://goo.gl/xmQnxf for more information and examples of labels.
// System reserved label keys are prefixed with "aiplatform.googleapis.com/"
// and are immutable. Following system labels exist for each DataLabelingJob:
//
// - "aiplatform.googleapis.com/schema": output only, its value is the
// [inputs_schema][google.cloud.aiplatform.v1beta1.DataLabelingJob.inputs_schema_uri]'s
// title.
Labels map[string]string `protobuf:"bytes,11,rep,name=labels,proto3" json:"labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// The SpecialistPools' resource names associated with this job.
SpecialistPools []string `protobuf:"bytes,16,rep,name=specialist_pools,json=specialistPools,proto3" json:"specialist_pools,omitempty"`
// Customer-managed encryption key spec for a DataLabelingJob. If set, this
// DataLabelingJob will be secured by this key.
//
// Note: Annotations created in the DataLabelingJob are associated with
// the EncryptionSpec of the Dataset they are exported to.
EncryptionSpec *EncryptionSpec `protobuf:"bytes,20,opt,name=encryption_spec,json=encryptionSpec,proto3" json:"encryption_spec,omitempty"`
// Parameters that configure the active learning pipeline. Active learning
// will label the data incrementally via several iterations. For every
// iteration, it will select a batch of data based on the sampling strategy.
ActiveLearningConfig *ActiveLearningConfig `protobuf:"bytes,21,opt,name=active_learning_config,json=activeLearningConfig,proto3" json:"active_learning_config,omitempty"`
}
func (x *DataLabelingJob) Reset() {
*x = DataLabelingJob{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *DataLabelingJob) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*DataLabelingJob) ProtoMessage() {}
func (x *DataLabelingJob) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use DataLabelingJob.ProtoReflect.Descriptor instead.
func (*DataLabelingJob) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescGZIP(), []int{0}
}
func (x *DataLabelingJob) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *DataLabelingJob) GetDisplayName() string {
if x != nil {
return x.DisplayName
}
return ""
}
func (x *DataLabelingJob) GetDatasets() []string {
if x != nil {
return x.Datasets
}
return nil
}
func (x *DataLabelingJob) GetAnnotationLabels() map[string]string {
if x != nil {
return x.AnnotationLabels
}
return nil
}
func (x *DataLabelingJob) GetLabelerCount() int32 {
if x != nil {
return x.LabelerCount
}
return 0
}
func (x *DataLabelingJob) GetInstructionUri() string {
if x != nil {
return x.InstructionUri
}
return ""
}
func (x *DataLabelingJob) GetInputsSchemaUri() string {
if x != nil {
return x.InputsSchemaUri
}
return ""
}
func (x *DataLabelingJob) GetInputs() *structpb.Value {
if x != nil {
return x.Inputs
}
return nil
}
func (x *DataLabelingJob) GetState() JobState {
if x != nil {
return x.State
}
return JobState_JOB_STATE_UNSPECIFIED
}
func (x *DataLabelingJob) GetLabelingProgress() int32 {
if x != nil {
return x.LabelingProgress
}
return 0
}
func (x *DataLabelingJob) GetCurrentSpend() *money.Money {
if x != nil {
return x.CurrentSpend
}
return nil
}
func (x *DataLabelingJob) GetCreateTime() *timestamppb.Timestamp {
if x != nil {
return x.CreateTime
}
return nil
}
func (x *DataLabelingJob) GetUpdateTime() *timestamppb.Timestamp {
if x != nil {
return x.UpdateTime
}
return nil
}
func (x *DataLabelingJob) GetError() *status.Status {
if x != nil {
return x.Error
}
return nil
}
func (x *DataLabelingJob) GetLabels() map[string]string {
if x != nil {
return x.Labels
}
return nil
}
func (x *DataLabelingJob) GetSpecialistPools() []string {
if x != nil {
return x.SpecialistPools
}
return nil
}
func (x *DataLabelingJob) GetEncryptionSpec() *EncryptionSpec {
if x != nil {
return x.EncryptionSpec
}
return nil
}
func (x *DataLabelingJob) GetActiveLearningConfig() *ActiveLearningConfig {
if x != nil {
return x.ActiveLearningConfig
}
return nil
}
// Parameters that configure the active learning pipeline. Active learning will
//
// label the data incrementally by several iterations. For every iteration, it
// will select a batch of data based on the sampling strategy.
type ActiveLearningConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Required. Max human labeling DataItems. The rest part will be labeled by
// machine.
//
// Types that are assignable to HumanLabelingBudget:
//
// *ActiveLearningConfig_MaxDataItemCount
// *ActiveLearningConfig_MaxDataItemPercentage
HumanLabelingBudget isActiveLearningConfig_HumanLabelingBudget `protobuf_oneof:"human_labeling_budget"`
// Active learning data sampling config. For every active learning labeling
// iteration, it will select a batch of data based on the sampling strategy.
SampleConfig *SampleConfig `protobuf:"bytes,3,opt,name=sample_config,json=sampleConfig,proto3" json:"sample_config,omitempty"`
// CMLE training config. For every active learning labeling iteration, system
// will train a machine learning model on CMLE. The trained model will be used
// by data sampling algorithm to select DataItems.
TrainingConfig *TrainingConfig `protobuf:"bytes,4,opt,name=training_config,json=trainingConfig,proto3" json:"training_config,omitempty"`
}
func (x *ActiveLearningConfig) Reset() {
*x = ActiveLearningConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ActiveLearningConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ActiveLearningConfig) ProtoMessage() {}
func (x *ActiveLearningConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ActiveLearningConfig.ProtoReflect.Descriptor instead.
func (*ActiveLearningConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescGZIP(), []int{1}
}
func (m *ActiveLearningConfig) GetHumanLabelingBudget() isActiveLearningConfig_HumanLabelingBudget {
if m != nil {
return m.HumanLabelingBudget
}
return nil
}
func (x *ActiveLearningConfig) GetMaxDataItemCount() int64 {
if x, ok := x.GetHumanLabelingBudget().(*ActiveLearningConfig_MaxDataItemCount); ok {
return x.MaxDataItemCount
}
return 0
}
func (x *ActiveLearningConfig) GetMaxDataItemPercentage() int32 {
if x, ok := x.GetHumanLabelingBudget().(*ActiveLearningConfig_MaxDataItemPercentage); ok {
return x.MaxDataItemPercentage
}
return 0
}
func (x *ActiveLearningConfig) GetSampleConfig() *SampleConfig {
if x != nil {
return x.SampleConfig
}
return nil
}
func (x *ActiveLearningConfig) GetTrainingConfig() *TrainingConfig {
if x != nil {
return x.TrainingConfig
}
return nil
}
type isActiveLearningConfig_HumanLabelingBudget interface {
isActiveLearningConfig_HumanLabelingBudget()
}
type ActiveLearningConfig_MaxDataItemCount struct {
// Max number of human labeled DataItems.
MaxDataItemCount int64 `protobuf:"varint,1,opt,name=max_data_item_count,json=maxDataItemCount,proto3,oneof"`
}
type ActiveLearningConfig_MaxDataItemPercentage struct {
// Max percent of total DataItems for human labeling.
MaxDataItemPercentage int32 `protobuf:"varint,2,opt,name=max_data_item_percentage,json=maxDataItemPercentage,proto3,oneof"`
}
func (*ActiveLearningConfig_MaxDataItemCount) isActiveLearningConfig_HumanLabelingBudget() {}
func (*ActiveLearningConfig_MaxDataItemPercentage) isActiveLearningConfig_HumanLabelingBudget() {}
// Active learning data sampling config. For every active learning labeling
// iteration, it will select a batch of data based on the sampling strategy.
type SampleConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Decides sample size for the initial batch. initial_batch_sample_percentage
// is used by default.
//
// Types that are assignable to InitialBatchSampleSize:
//
// *SampleConfig_InitialBatchSamplePercentage
InitialBatchSampleSize isSampleConfig_InitialBatchSampleSize `protobuf_oneof:"initial_batch_sample_size"`
// Decides sample size for the following batches.
// following_batch_sample_percentage is used by default.
//
// Types that are assignable to FollowingBatchSampleSize:
//
// *SampleConfig_FollowingBatchSamplePercentage
FollowingBatchSampleSize isSampleConfig_FollowingBatchSampleSize `protobuf_oneof:"following_batch_sample_size"`
// Field to choose sampling strategy. Sampling strategy will decide which data
// should be selected for human labeling in every batch.
SampleStrategy SampleConfig_SampleStrategy `protobuf:"varint,5,opt,name=sample_strategy,json=sampleStrategy,proto3,enum=google.cloud.aiplatform.v1beta1.SampleConfig_SampleStrategy" json:"sample_strategy,omitempty"`
}
func (x *SampleConfig) Reset() {
*x = SampleConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *SampleConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*SampleConfig) ProtoMessage() {}
func (x *SampleConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use SampleConfig.ProtoReflect.Descriptor instead.
func (*SampleConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescGZIP(), []int{2}
}
func (m *SampleConfig) GetInitialBatchSampleSize() isSampleConfig_InitialBatchSampleSize {
if m != nil {
return m.InitialBatchSampleSize
}
return nil
}
func (x *SampleConfig) GetInitialBatchSamplePercentage() int32 {
if x, ok := x.GetInitialBatchSampleSize().(*SampleConfig_InitialBatchSamplePercentage); ok {
return x.InitialBatchSamplePercentage
}
return 0
}
func (m *SampleConfig) GetFollowingBatchSampleSize() isSampleConfig_FollowingBatchSampleSize {
if m != nil {
return m.FollowingBatchSampleSize
}
return nil
}
func (x *SampleConfig) GetFollowingBatchSamplePercentage() int32 {
if x, ok := x.GetFollowingBatchSampleSize().(*SampleConfig_FollowingBatchSamplePercentage); ok {
return x.FollowingBatchSamplePercentage
}
return 0
}
func (x *SampleConfig) GetSampleStrategy() SampleConfig_SampleStrategy {
if x != nil {
return x.SampleStrategy
}
return SampleConfig_SAMPLE_STRATEGY_UNSPECIFIED
}
type isSampleConfig_InitialBatchSampleSize interface {
isSampleConfig_InitialBatchSampleSize()
}
type SampleConfig_InitialBatchSamplePercentage struct {
// The percentage of data needed to be labeled in the first batch.
InitialBatchSamplePercentage int32 `protobuf:"varint,1,opt,name=initial_batch_sample_percentage,json=initialBatchSamplePercentage,proto3,oneof"`
}
func (*SampleConfig_InitialBatchSamplePercentage) isSampleConfig_InitialBatchSampleSize() {}
type isSampleConfig_FollowingBatchSampleSize interface {
isSampleConfig_FollowingBatchSampleSize()
}
type SampleConfig_FollowingBatchSamplePercentage struct {
// The percentage of data needed to be labeled in each following batch
// (except the first batch).
FollowingBatchSamplePercentage int32 `protobuf:"varint,3,opt,name=following_batch_sample_percentage,json=followingBatchSamplePercentage,proto3,oneof"`
}
func (*SampleConfig_FollowingBatchSamplePercentage) isSampleConfig_FollowingBatchSampleSize() {}
// CMLE training config. For every active learning labeling iteration, system
// will train a machine learning model on CMLE. The trained model will be used
// by data sampling algorithm to select DataItems.
type TrainingConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The timeout hours for the CMLE training job, expressed in milli hours
// i.e. 1,000 value in this field means 1 hour.
TimeoutTrainingMilliHours int64 `protobuf:"varint,1,opt,name=timeout_training_milli_hours,json=timeoutTrainingMilliHours,proto3" json:"timeout_training_milli_hours,omitempty"`
}
func (x *TrainingConfig) Reset() {
*x = TrainingConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TrainingConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TrainingConfig) ProtoMessage() {}
func (x *TrainingConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TrainingConfig.ProtoReflect.Descriptor instead.
func (*TrainingConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescGZIP(), []int{3}
}
func (x *TrainingConfig) GetTimeoutTrainingMilliHours() int64 {
if x != nil {
return x.TimeoutTrainingMilliHours
}
return 0
}
var File_google_cloud_aiplatform_v1beta1_data_labeling_job_proto protoreflect.FileDescriptor
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}
var (
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescOnce sync.Once
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescData = file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDesc
)
func file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescGZIP() []byte {
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescOnce.Do(func() {
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescData)
})
return file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDescData
}
var file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes = make([]protoimpl.MessageInfo, 6)
var file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_goTypes = []interface{}{
(SampleConfig_SampleStrategy)(0), // 0: google.cloud.aiplatform.v1beta1.SampleConfig.SampleStrategy
(*DataLabelingJob)(nil), // 1: google.cloud.aiplatform.v1beta1.DataLabelingJob
(*ActiveLearningConfig)(nil), // 2: google.cloud.aiplatform.v1beta1.ActiveLearningConfig
(*SampleConfig)(nil), // 3: google.cloud.aiplatform.v1beta1.SampleConfig
(*TrainingConfig)(nil), // 4: google.cloud.aiplatform.v1beta1.TrainingConfig
nil, // 5: google.cloud.aiplatform.v1beta1.DataLabelingJob.AnnotationLabelsEntry
nil, // 6: google.cloud.aiplatform.v1beta1.DataLabelingJob.LabelsEntry
(*structpb.Value)(nil), // 7: google.protobuf.Value
(JobState)(0), // 8: google.cloud.aiplatform.v1beta1.JobState
(*money.Money)(nil), // 9: google.type.Money
(*timestamppb.Timestamp)(nil), // 10: google.protobuf.Timestamp
(*status.Status)(nil), // 11: google.rpc.Status
(*EncryptionSpec)(nil), // 12: google.cloud.aiplatform.v1beta1.EncryptionSpec
}
var file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_depIdxs = []int32{
5, // 0: google.cloud.aiplatform.v1beta1.DataLabelingJob.annotation_labels:type_name -> google.cloud.aiplatform.v1beta1.DataLabelingJob.AnnotationLabelsEntry
7, // 1: google.cloud.aiplatform.v1beta1.DataLabelingJob.inputs:type_name -> google.protobuf.Value
8, // 2: google.cloud.aiplatform.v1beta1.DataLabelingJob.state:type_name -> google.cloud.aiplatform.v1beta1.JobState
9, // 3: google.cloud.aiplatform.v1beta1.DataLabelingJob.current_spend:type_name -> google.type.Money
10, // 4: google.cloud.aiplatform.v1beta1.DataLabelingJob.create_time:type_name -> google.protobuf.Timestamp
10, // 5: google.cloud.aiplatform.v1beta1.DataLabelingJob.update_time:type_name -> google.protobuf.Timestamp
11, // 6: google.cloud.aiplatform.v1beta1.DataLabelingJob.error:type_name -> google.rpc.Status
6, // 7: google.cloud.aiplatform.v1beta1.DataLabelingJob.labels:type_name -> google.cloud.aiplatform.v1beta1.DataLabelingJob.LabelsEntry
12, // 8: google.cloud.aiplatform.v1beta1.DataLabelingJob.encryption_spec:type_name -> google.cloud.aiplatform.v1beta1.EncryptionSpec
2, // 9: google.cloud.aiplatform.v1beta1.DataLabelingJob.active_learning_config:type_name -> google.cloud.aiplatform.v1beta1.ActiveLearningConfig
3, // 10: google.cloud.aiplatform.v1beta1.ActiveLearningConfig.sample_config:type_name -> google.cloud.aiplatform.v1beta1.SampleConfig
4, // 11: google.cloud.aiplatform.v1beta1.ActiveLearningConfig.training_config:type_name -> google.cloud.aiplatform.v1beta1.TrainingConfig
0, // 12: google.cloud.aiplatform.v1beta1.SampleConfig.sample_strategy:type_name -> google.cloud.aiplatform.v1beta1.SampleConfig.SampleStrategy
13, // [13:13] is the sub-list for method output_type
13, // [13:13] is the sub-list for method input_type
13, // [13:13] is the sub-list for extension type_name
13, // [13:13] is the sub-list for extension extendee
0, // [0:13] is the sub-list for field type_name
}
func init() { file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_init() }
func file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_init() {
if File_google_cloud_aiplatform_v1beta1_data_labeling_job_proto != nil {
return
}
file_google_cloud_aiplatform_v1beta1_encryption_spec_proto_init()
file_google_cloud_aiplatform_v1beta1_job_state_proto_init()
if !protoimpl.UnsafeEnabled {
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*DataLabelingJob); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ActiveLearningConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*SampleConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TrainingConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[1].OneofWrappers = []interface{}{
(*ActiveLearningConfig_MaxDataItemCount)(nil),
(*ActiveLearningConfig_MaxDataItemPercentage)(nil),
}
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes[2].OneofWrappers = []interface{}{
(*SampleConfig_InitialBatchSamplePercentage)(nil),
(*SampleConfig_FollowingBatchSamplePercentage)(nil),
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDesc,
NumEnums: 1,
NumMessages: 6,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_goTypes,
DependencyIndexes: file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_depIdxs,
EnumInfos: file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_enumTypes,
MessageInfos: file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_msgTypes,
}.Build()
File_google_cloud_aiplatform_v1beta1_data_labeling_job_proto = out.File
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_rawDesc = nil
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_goTypes = nil
file_google_cloud_aiplatform_v1beta1_data_labeling_job_proto_depIdxs = nil
}