// Copyright 2019 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 v1.22.0
// protoc v3.11.2
// source: google/cloud/datalabeling/v1beta1/evaluation_job.proto
package datalabeling
import (
reflect "reflect"
sync "sync"
proto "github.com/golang/protobuf/proto"
timestamp "github.com/golang/protobuf/ptypes/timestamp"
_ "google.golang.org/genproto/googleapis/api/annotations"
status "google.golang.org/genproto/googleapis/rpc/status"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
)
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)
)
// This is a compile-time assertion that a sufficiently up-to-date version
// of the legacy proto package is being used.
const _ = proto.ProtoPackageIsVersion4
// State of the job.
type EvaluationJob_State int32
const (
EvaluationJob_STATE_UNSPECIFIED EvaluationJob_State = 0
// The job is scheduled to run at the [configured interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. You
// can [pause][google.cloud.datalabeling.v1beta1.DataLabelingService.PauseEvaluationJob] or
// [delete][google.cloud.datalabeling.v1beta1.DataLabelingService.DeleteEvaluationJob] the job.
//
// When the job is in this state, it samples prediction input and output
// from your model version into your BigQuery table as predictions occur.
EvaluationJob_SCHEDULED EvaluationJob_State = 1
// The job is currently running. When the job runs, Data Labeling Service
// does several things:
//
// 1. If you have configured your job to use Data Labeling Service for
// ground truth labeling, the service creates a
// [Dataset][google.cloud.datalabeling.v1beta1.Dataset] and a labeling task for all data sampled
// since the last time the job ran. Human labelers provide ground truth
// labels for your data. Human labeling may take hours, or even days,
// depending on how much data has been sampled. The job remains in the
// `RUNNING` state during this time, and it can even be running multiple
// times in parallel if it gets triggered again (for example 24 hours
// later) before the earlier run has completed. When human labelers have
// finished labeling the data, the next step occurs.
//
// If you have configured your job to provide your own ground truth
// labels, Data Labeling Service still creates a [Dataset][google.cloud.datalabeling.v1beta1.Dataset] for newly
// sampled data, but it expects that you have already added ground truth
// labels to the BigQuery table by this time. The next step occurs
// immediately.
//
// 2. Data Labeling Service creates an [Evaluation][google.cloud.datalabeling.v1beta1.Evaluation] by comparing your
// model version's predictions with the ground truth labels.
//
// If the job remains in this state for a long time, it continues to sample
// prediction data into your BigQuery table and will run again at the next
// interval, even if it causes the job to run multiple times in parallel.
EvaluationJob_RUNNING EvaluationJob_State = 2
// The job is not sampling prediction input and output into your BigQuery
// table and it will not run according to its schedule. You can
// [resume][google.cloud.datalabeling.v1beta1.DataLabelingService.ResumeEvaluationJob] the job.
EvaluationJob_PAUSED EvaluationJob_State = 3
// The job has this state right before it is deleted.
EvaluationJob_STOPPED EvaluationJob_State = 4
)
// Enum value maps for EvaluationJob_State.
var (
EvaluationJob_State_name = map[int32]string{
0: "STATE_UNSPECIFIED",
1: "SCHEDULED",
2: "RUNNING",
3: "PAUSED",
4: "STOPPED",
}
EvaluationJob_State_value = map[string]int32{
"STATE_UNSPECIFIED": 0,
"SCHEDULED": 1,
"RUNNING": 2,
"PAUSED": 3,
"STOPPED": 4,
}
)
func (x EvaluationJob_State) Enum() *EvaluationJob_State {
p := new(EvaluationJob_State)
*p = x
return p
}
func (x EvaluationJob_State) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (EvaluationJob_State) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_enumTypes[0].Descriptor()
}
func (EvaluationJob_State) Type() protoreflect.EnumType {
return &file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_enumTypes[0]
}
func (x EvaluationJob_State) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use EvaluationJob_State.Descriptor instead.
func (EvaluationJob_State) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescGZIP(), []int{0, 0}
}
// Defines an evaluation job that runs periodically to generate
// [Evaluations][google.cloud.datalabeling.v1beta1.Evaluation]. [Creating an evaluation
// job](/ml-engine/docs/continuous-evaluation/create-job) is the starting point
// for using continuous evaluation.
type EvaluationJob struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. After you create a job, Data Labeling Service assigns a name
// to the job with the following format:
//
// "projects/{project_id}/evaluationJobs/{evaluation_job_id}"
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Required. Description of the job. The description can be up to 25,000
// characters long.
Description string `protobuf:"bytes,2,opt,name=description,proto3" json:"description,omitempty"`
// Output only. Describes the current state of the job.
State EvaluationJob_State `protobuf:"varint,3,opt,name=state,proto3,enum=google.cloud.datalabeling.v1beta1.EvaluationJob_State" json:"state,omitempty"`
// Required. Describes the interval at which the job runs. This interval must
// be at least 1 day, and it is rounded to the nearest day. For example, if
// you specify a 50-hour interval, the job runs every 2 days.
//
// You can provide the schedule in
// [crontab format](/scheduler/docs/configuring/cron-job-schedules) or in an
// [English-like
// format](/appengine/docs/standard/python/config/cronref#schedule_format).
//
// Regardless of what you specify, the job will run at 10:00 AM UTC. Only the
// interval from this schedule is used, not the specific time of day.
Schedule string `protobuf:"bytes,4,opt,name=schedule,proto3" json:"schedule,omitempty"`
// Required. The [AI Platform Prediction model
// version](/ml-engine/docs/prediction-overview) to be evaluated. Prediction
// input and output is sampled from this model version. When creating an
// evaluation job, specify the model version in the following format:
//
// "projects/{project_id}/models/{model_name}/versions/{version_name}"
//
// There can only be one evaluation job per model version.
ModelVersion string `protobuf:"bytes,5,opt,name=model_version,json=modelVersion,proto3" json:"model_version,omitempty"`
// Required. Configuration details for the evaluation job.
EvaluationJobConfig *EvaluationJobConfig `protobuf:"bytes,6,opt,name=evaluation_job_config,json=evaluationJobConfig,proto3" json:"evaluation_job_config,omitempty"`
// Required. Name of the [AnnotationSpecSet][google.cloud.datalabeling.v1beta1.AnnotationSpecSet] describing all the
// labels that your machine learning model outputs. You must create this
// resource before you create an evaluation job and provide its name in the
// following format:
//
// "projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}"
AnnotationSpecSet string `protobuf:"bytes,7,opt,name=annotation_spec_set,json=annotationSpecSet,proto3" json:"annotation_spec_set,omitempty"`
// Required. Whether you want Data Labeling Service to provide ground truth
// labels for prediction input. If you want the service to assign human
// labelers to annotate your data, set this to `true`. If you want to provide
// your own ground truth labels in the evaluation job's BigQuery table, set
// this to `false`.
LabelMissingGroundTruth bool `protobuf:"varint,8,opt,name=label_missing_ground_truth,json=labelMissingGroundTruth,proto3" json:"label_missing_ground_truth,omitempty"`
// Output only. Every time the evaluation job runs and an error occurs, the
// failed attempt is appended to this array.
Attempts []*Attempt `protobuf:"bytes,9,rep,name=attempts,proto3" json:"attempts,omitempty"`
// Output only. Timestamp of when this evaluation job was created.
CreateTime *timestamp.Timestamp `protobuf:"bytes,10,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
}
func (x *EvaluationJob) Reset() {
*x = EvaluationJob{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *EvaluationJob) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*EvaluationJob) ProtoMessage() {}
func (x *EvaluationJob) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_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 EvaluationJob.ProtoReflect.Descriptor instead.
func (*EvaluationJob) Descriptor() ([]byte, []int) {
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescGZIP(), []int{0}
}
func (x *EvaluationJob) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *EvaluationJob) GetDescription() string {
if x != nil {
return x.Description
}
return ""
}
func (x *EvaluationJob) GetState() EvaluationJob_State {
if x != nil {
return x.State
}
return EvaluationJob_STATE_UNSPECIFIED
}
func (x *EvaluationJob) GetSchedule() string {
if x != nil {
return x.Schedule
}
return ""
}
func (x *EvaluationJob) GetModelVersion() string {
if x != nil {
return x.ModelVersion
}
return ""
}
func (x *EvaluationJob) GetEvaluationJobConfig() *EvaluationJobConfig {
if x != nil {
return x.EvaluationJobConfig
}
return nil
}
func (x *EvaluationJob) GetAnnotationSpecSet() string {
if x != nil {
return x.AnnotationSpecSet
}
return ""
}
func (x *EvaluationJob) GetLabelMissingGroundTruth() bool {
if x != nil {
return x.LabelMissingGroundTruth
}
return false
}
func (x *EvaluationJob) GetAttempts() []*Attempt {
if x != nil {
return x.Attempts
}
return nil
}
func (x *EvaluationJob) GetCreateTime() *timestamp.Timestamp {
if x != nil {
return x.CreateTime
}
return nil
}
// Configures specific details of how a continuous evaluation job works. Provide
// this configuration when you create an EvaluationJob.
type EvaluationJobConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Required. Details for how you want human reviewers to provide ground truth
// labels.
//
// Types that are assignable to HumanAnnotationRequestConfig:
// *EvaluationJobConfig_ImageClassificationConfig
// *EvaluationJobConfig_BoundingPolyConfig
// *EvaluationJobConfig_TextClassificationConfig
HumanAnnotationRequestConfig isEvaluationJobConfig_HumanAnnotationRequestConfig `protobuf_oneof:"human_annotation_request_config"`
// Rquired. Details for the sampled prediction input. Within this
// configuration, there are requirements for several fields:
//
// * `dataType` must be one of `IMAGE`, `TEXT`, or `GENERAL_DATA`.
// * `annotationType` must be one of `IMAGE_CLASSIFICATION_ANNOTATION`,
// `TEXT_CLASSIFICATION_ANNOTATION`, `GENERAL_CLASSIFICATION_ANNOTATION`,
// or `IMAGE_BOUNDING_BOX_ANNOTATION` (image object detection).
// * If your machine learning model performs classification, you must specify
// `classificationMetadata.isMultiLabel`.
// * You must specify `bigquerySource` (not `gcsSource`).
InputConfig *InputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
// Required. Details for calculating evaluation metrics and creating
// [Evaulations][google.cloud.datalabeling.v1beta1.Evaluation]. If your model version performs image object
// detection, you must specify the `boundingBoxEvaluationOptions` field within
// this configuration. Otherwise, provide an empty object for this
// configuration.
EvaluationConfig *EvaluationConfig `protobuf:"bytes,2,opt,name=evaluation_config,json=evaluationConfig,proto3" json:"evaluation_config,omitempty"`
// Optional. Details for human annotation of your data. If you set
// [labelMissingGroundTruth][google.cloud.datalabeling.v1beta1.EvaluationJob.label_missing_ground_truth] to
// `true` for this evaluation job, then you must specify this field. If you
// plan to provide your own ground truth labels, then omit this field.
//
// Note that you must create an [Instruction][google.cloud.datalabeling.v1beta1.Instruction] resource before you can
// specify this field. Provide the name of the instruction resource in the
// `instruction` field within this configuration.
HumanAnnotationConfig *HumanAnnotationConfig `protobuf:"bytes,3,opt,name=human_annotation_config,json=humanAnnotationConfig,proto3" json:"human_annotation_config,omitempty"`
// Required. Prediction keys that tell Data Labeling Service where to find the
// data for evaluation in your BigQuery table. When the service samples
// prediction input and output from your model version and saves it to
// BigQuery, the data gets stored as JSON strings in the BigQuery table. These
// keys tell Data Labeling Service how to parse the JSON.
//
// You can provide the following entries in this field:
//
// * `data_json_key`: the data key for prediction input. You must provide
// either this key or `reference_json_key`.
// * `reference_json_key`: the data reference key for prediction input. You
// must provide either this key or `data_json_key`.
// * `label_json_key`: the label key for prediction output. Required.
// * `label_score_json_key`: the score key for prediction output. Required.
// * `bounding_box_json_key`: the bounding box key for prediction output.
// Required if your model version perform image object detection.
//
// Learn [how to configure prediction
// keys](/ml-engine/docs/continuous-evaluation/create-job#prediction-keys).
BigqueryImportKeys map[string]string `protobuf:"bytes,9,rep,name=bigquery_import_keys,json=bigqueryImportKeys,proto3" json:"bigquery_import_keys,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
// Required. The maximum number of predictions to sample and save to BigQuery
// during each [evaluation interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. This limit
// overrides `example_sample_percentage`: even if the service has not sampled
// enough predictions to fulfill `example_sample_perecentage` during an
// interval, it stops sampling predictions when it meets this limit.
ExampleCount int32 `protobuf:"varint,10,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
// Required. Fraction of predictions to sample and save to BigQuery during
// each [evaluation interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. For example, 0.1 means
// 10% of predictions served by your model version get saved to BigQuery.
ExampleSamplePercentage float64 `protobuf:"fixed64,11,opt,name=example_sample_percentage,json=exampleSamplePercentage,proto3" json:"example_sample_percentage,omitempty"`
// Optional. Configuration details for evaluation job alerts. Specify this
// field if you want to receive email alerts if the evaluation job finds that
// your predictions have low mean average precision during a run.
EvaluationJobAlertConfig *EvaluationJobAlertConfig `protobuf:"bytes,13,opt,name=evaluation_job_alert_config,json=evaluationJobAlertConfig,proto3" json:"evaluation_job_alert_config,omitempty"`
}
func (x *EvaluationJobConfig) Reset() {
*x = EvaluationJobConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *EvaluationJobConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*EvaluationJobConfig) ProtoMessage() {}
func (x *EvaluationJobConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_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 EvaluationJobConfig.ProtoReflect.Descriptor instead.
func (*EvaluationJobConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescGZIP(), []int{1}
}
func (m *EvaluationJobConfig) GetHumanAnnotationRequestConfig() isEvaluationJobConfig_HumanAnnotationRequestConfig {
if m != nil {
return m.HumanAnnotationRequestConfig
}
return nil
}
func (x *EvaluationJobConfig) GetImageClassificationConfig() *ImageClassificationConfig {
if x, ok := x.GetHumanAnnotationRequestConfig().(*EvaluationJobConfig_ImageClassificationConfig); ok {
return x.ImageClassificationConfig
}
return nil
}
func (x *EvaluationJobConfig) GetBoundingPolyConfig() *BoundingPolyConfig {
if x, ok := x.GetHumanAnnotationRequestConfig().(*EvaluationJobConfig_BoundingPolyConfig); ok {
return x.BoundingPolyConfig
}
return nil
}
func (x *EvaluationJobConfig) GetTextClassificationConfig() *TextClassificationConfig {
if x, ok := x.GetHumanAnnotationRequestConfig().(*EvaluationJobConfig_TextClassificationConfig); ok {
return x.TextClassificationConfig
}
return nil
}
func (x *EvaluationJobConfig) GetInputConfig() *InputConfig {
if x != nil {
return x.InputConfig
}
return nil
}
func (x *EvaluationJobConfig) GetEvaluationConfig() *EvaluationConfig {
if x != nil {
return x.EvaluationConfig
}
return nil
}
func (x *EvaluationJobConfig) GetHumanAnnotationConfig() *HumanAnnotationConfig {
if x != nil {
return x.HumanAnnotationConfig
}
return nil
}
func (x *EvaluationJobConfig) GetBigqueryImportKeys() map[string]string {
if x != nil {
return x.BigqueryImportKeys
}
return nil
}
func (x *EvaluationJobConfig) GetExampleCount() int32 {
if x != nil {
return x.ExampleCount
}
return 0
}
func (x *EvaluationJobConfig) GetExampleSamplePercentage() float64 {
if x != nil {
return x.ExampleSamplePercentage
}
return 0
}
func (x *EvaluationJobConfig) GetEvaluationJobAlertConfig() *EvaluationJobAlertConfig {
if x != nil {
return x.EvaluationJobAlertConfig
}
return nil
}
type isEvaluationJobConfig_HumanAnnotationRequestConfig interface {
isEvaluationJobConfig_HumanAnnotationRequestConfig()
}
type EvaluationJobConfig_ImageClassificationConfig struct {
// Specify this field if your model version performs image classification or
// general classification.
//
// `annotationSpecSet` in this configuration must match
// [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
// `allowMultiLabel` in this configuration must match
// `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
ImageClassificationConfig *ImageClassificationConfig `protobuf:"bytes,4,opt,name=image_classification_config,json=imageClassificationConfig,proto3,oneof"`
}
type EvaluationJobConfig_BoundingPolyConfig struct {
// Specify this field if your model version performs image object detection
// (bounding box detection).
//
// `annotationSpecSet` in this configuration must match
// [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
BoundingPolyConfig *BoundingPolyConfig `protobuf:"bytes,5,opt,name=bounding_poly_config,json=boundingPolyConfig,proto3,oneof"`
}
type EvaluationJobConfig_TextClassificationConfig struct {
// Specify this field if your model version performs text classification.
//
// `annotationSpecSet` in this configuration must match
// [EvaluationJob.annotationSpecSet][google.cloud.datalabeling.v1beta1.EvaluationJob.annotation_spec_set].
// `allowMultiLabel` in this configuration must match
// `classificationMetadata.isMultiLabel` in [input_config][google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config].
TextClassificationConfig *TextClassificationConfig `protobuf:"bytes,8,opt,name=text_classification_config,json=textClassificationConfig,proto3,oneof"`
}
func (*EvaluationJobConfig_ImageClassificationConfig) isEvaluationJobConfig_HumanAnnotationRequestConfig() {
}
func (*EvaluationJobConfig_BoundingPolyConfig) isEvaluationJobConfig_HumanAnnotationRequestConfig() {}
func (*EvaluationJobConfig_TextClassificationConfig) isEvaluationJobConfig_HumanAnnotationRequestConfig() {
}
// Provides details for how an evaluation job sends email alerts based on the
// results of a run.
type EvaluationJobAlertConfig struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Required. An email address to send alerts to.
Email string `protobuf:"bytes,1,opt,name=email,proto3" json:"email,omitempty"`
// Required. A number between 0 and 1 that describes a minimum mean average
// precision threshold. When the evaluation job runs, if it calculates that
// your model version's predictions from the recent interval have
// [meanAveragePrecision][google.cloud.datalabeling.v1beta1.PrCurve.mean_average_precision] below this
// threshold, then it sends an alert to your specified email.
MinAcceptableMeanAveragePrecision float64 `protobuf:"fixed64,2,opt,name=min_acceptable_mean_average_precision,json=minAcceptableMeanAveragePrecision,proto3" json:"min_acceptable_mean_average_precision,omitempty"`
}
func (x *EvaluationJobAlertConfig) Reset() {
*x = EvaluationJobAlertConfig{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *EvaluationJobAlertConfig) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*EvaluationJobAlertConfig) ProtoMessage() {}
func (x *EvaluationJobAlertConfig) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_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 EvaluationJobAlertConfig.ProtoReflect.Descriptor instead.
func (*EvaluationJobAlertConfig) Descriptor() ([]byte, []int) {
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescGZIP(), []int{2}
}
func (x *EvaluationJobAlertConfig) GetEmail() string {
if x != nil {
return x.Email
}
return ""
}
func (x *EvaluationJobAlertConfig) GetMinAcceptableMeanAveragePrecision() float64 {
if x != nil {
return x.MinAcceptableMeanAveragePrecision
}
return 0
}
// Records a failed evaluation job run.
type Attempt struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
AttemptTime *timestamp.Timestamp `protobuf:"bytes,1,opt,name=attempt_time,json=attemptTime,proto3" json:"attempt_time,omitempty"`
// Details of errors that occurred.
PartialFailures []*status.Status `protobuf:"bytes,2,rep,name=partial_failures,json=partialFailures,proto3" json:"partial_failures,omitempty"`
}
func (x *Attempt) Reset() {
*x = Attempt{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *Attempt) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*Attempt) ProtoMessage() {}
func (x *Attempt) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_datalabeling_v1beta1_evaluation_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 Attempt.ProtoReflect.Descriptor instead.
func (*Attempt) Descriptor() ([]byte, []int) {
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescGZIP(), []int{3}
}
func (x *Attempt) GetAttemptTime() *timestamp.Timestamp {
if x != nil {
return x.AttemptTime
}
return nil
}
func (x *Attempt) GetPartialFailures() []*status.Status {
if x != nil {
return x.PartialFailures
}
return nil
}
var File_google_cloud_datalabeling_v1beta1_evaluation_job_proto protoreflect.FileDescriptor
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}
var (
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescOnce sync.Once
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescData = file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDesc
)
func file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescGZIP() []byte {
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescOnce.Do(func() {
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescData)
})
return file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDescData
}
var file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes = make([]protoimpl.MessageInfo, 5)
var file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_goTypes = []interface{}{
(EvaluationJob_State)(0), // 0: google.cloud.datalabeling.v1beta1.EvaluationJob.State
(*EvaluationJob)(nil), // 1: google.cloud.datalabeling.v1beta1.EvaluationJob
(*EvaluationJobConfig)(nil), // 2: google.cloud.datalabeling.v1beta1.EvaluationJobConfig
(*EvaluationJobAlertConfig)(nil), // 3: google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig
(*Attempt)(nil), // 4: google.cloud.datalabeling.v1beta1.Attempt
nil, // 5: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.BigqueryImportKeysEntry
(*timestamp.Timestamp)(nil), // 6: google.protobuf.Timestamp
(*ImageClassificationConfig)(nil), // 7: google.cloud.datalabeling.v1beta1.ImageClassificationConfig
(*BoundingPolyConfig)(nil), // 8: google.cloud.datalabeling.v1beta1.BoundingPolyConfig
(*TextClassificationConfig)(nil), // 9: google.cloud.datalabeling.v1beta1.TextClassificationConfig
(*InputConfig)(nil), // 10: google.cloud.datalabeling.v1beta1.InputConfig
(*EvaluationConfig)(nil), // 11: google.cloud.datalabeling.v1beta1.EvaluationConfig
(*HumanAnnotationConfig)(nil), // 12: google.cloud.datalabeling.v1beta1.HumanAnnotationConfig
(*status.Status)(nil), // 13: google.rpc.Status
}
var file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_depIdxs = []int32{
0, // 0: google.cloud.datalabeling.v1beta1.EvaluationJob.state:type_name -> google.cloud.datalabeling.v1beta1.EvaluationJob.State
2, // 1: google.cloud.datalabeling.v1beta1.EvaluationJob.evaluation_job_config:type_name -> google.cloud.datalabeling.v1beta1.EvaluationJobConfig
4, // 2: google.cloud.datalabeling.v1beta1.EvaluationJob.attempts:type_name -> google.cloud.datalabeling.v1beta1.Attempt
6, // 3: google.cloud.datalabeling.v1beta1.EvaluationJob.create_time:type_name -> google.protobuf.Timestamp
7, // 4: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.image_classification_config:type_name -> google.cloud.datalabeling.v1beta1.ImageClassificationConfig
8, // 5: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.bounding_poly_config:type_name -> google.cloud.datalabeling.v1beta1.BoundingPolyConfig
9, // 6: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.text_classification_config:type_name -> google.cloud.datalabeling.v1beta1.TextClassificationConfig
10, // 7: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.input_config:type_name -> google.cloud.datalabeling.v1beta1.InputConfig
11, // 8: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.evaluation_config:type_name -> google.cloud.datalabeling.v1beta1.EvaluationConfig
12, // 9: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.human_annotation_config:type_name -> google.cloud.datalabeling.v1beta1.HumanAnnotationConfig
5, // 10: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.bigquery_import_keys:type_name -> google.cloud.datalabeling.v1beta1.EvaluationJobConfig.BigqueryImportKeysEntry
3, // 11: google.cloud.datalabeling.v1beta1.EvaluationJobConfig.evaluation_job_alert_config:type_name -> google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig
6, // 12: google.cloud.datalabeling.v1beta1.Attempt.attempt_time:type_name -> google.protobuf.Timestamp
13, // 13: google.cloud.datalabeling.v1beta1.Attempt.partial_failures:type_name -> google.rpc.Status
14, // [14:14] is the sub-list for method output_type
14, // [14:14] is the sub-list for method input_type
14, // [14:14] is the sub-list for extension type_name
14, // [14:14] is the sub-list for extension extendee
0, // [0:14] is the sub-list for field type_name
}
func init() { file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_init() }
func file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_init() {
if File_google_cloud_datalabeling_v1beta1_evaluation_job_proto != nil {
return
}
file_google_cloud_datalabeling_v1beta1_dataset_proto_init()
file_google_cloud_datalabeling_v1beta1_evaluation_proto_init()
file_google_cloud_datalabeling_v1beta1_human_annotation_config_proto_init()
if !protoimpl.UnsafeEnabled {
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*EvaluationJob); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*EvaluationJobConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*EvaluationJobAlertConfig); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*Attempt); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes[1].OneofWrappers = []interface{}{
(*EvaluationJobConfig_ImageClassificationConfig)(nil),
(*EvaluationJobConfig_BoundingPolyConfig)(nil),
(*EvaluationJobConfig_TextClassificationConfig)(nil),
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDesc,
NumEnums: 1,
NumMessages: 5,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_goTypes,
DependencyIndexes: file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_depIdxs,
EnumInfos: file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_enumTypes,
MessageInfos: file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_msgTypes,
}.Build()
File_google_cloud_datalabeling_v1beta1_evaluation_job_proto = out.File
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_rawDesc = nil
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_goTypes = nil
file_google_cloud_datalabeling_v1beta1_evaluation_job_proto_depIdxs = nil
}