Package cloud.google.com/go/automl/apiv1/automlpb (v1.13.4)

var (
	ClassificationType_name = map[int32]string{
		0: "CLASSIFICATION_TYPE_UNSPECIFIED",
		1: "MULTICLASS",
		2: "MULTILABEL",
	}
	ClassificationType_value = map[string]int32{
		"CLASSIFICATION_TYPE_UNSPECIFIED": 0,
		"MULTICLASS":                      1,
		"MULTILABEL":                      2,
	}
)

var (
	DocumentDimensions_DocumentDimensionUnit_name = map[int32]string{
		0: "DOCUMENT_DIMENSION_UNIT_UNSPECIFIED",
		1: "INCH",
		2: "CENTIMETER",
		3: "POINT",
	}
	DocumentDimensions_DocumentDimensionUnit_value = map[string]int32{
		"DOCUMENT_DIMENSION_UNIT_UNSPECIFIED": 0,
		"INCH":                                1,
		"CENTIMETER":                          2,
		"POINT":                               3,
	}
)

var (
	Document_Layout_TextSegmentType_name = map[int32]string{
		0: "TEXT_SEGMENT_TYPE_UNSPECIFIED",
		1: "TOKEN",
		2: "PARAGRAPH",
		3: "FORM_FIELD",
		4: "FORM_FIELD_NAME",
		5: "FORM_FIELD_CONTENTS",
		6: "TABLE",
		7: "TABLE_HEADER",
		8: "TABLE_ROW",
		9: "TABLE_CELL",
	}
	Document_Layout_TextSegmentType_value = map[string]int32{
		"TEXT_SEGMENT_TYPE_UNSPECIFIED": 0,
		"TOKEN":                         1,
		"PARAGRAPH":                     2,
		"FORM_FIELD":                    3,
		"FORM_FIELD_NAME":               4,
		"FORM_FIELD_CONTENTS":           5,
		"TABLE":                         6,
		"TABLE_HEADER":                  7,
		"TABLE_ROW":                     8,
		"TABLE_CELL":                    9,
	}
)

var (
	Model_DeploymentState_name = map[int32]string{
		0: "DEPLOYMENT_STATE_UNSPECIFIED",
		1: "DEPLOYED",
		2: "UNDEPLOYED",
	}
	Model_DeploymentState_value = map[string]int32{
		"DEPLOYMENT_STATE_UNSPECIFIED": 0,
		"DEPLOYED":                     1,
		"UNDEPLOYED":                   2,
	}
)

var File_google_cloud_automl_v1_annotation_payload_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_annotation_spec_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_classification_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_data_items_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_dataset_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_detection_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_geometry_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_image_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_io_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_model_evaluation_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_model_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_operations_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_prediction_service_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_service_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_text_extraction_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_text_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_text_segment_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_text_sentiment_proto protoreflect.FileDescriptor

var File_google_cloud_automl_v1_translation_proto protoreflect.FileDescriptor

func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer)

func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer)

type AnnotationPayload struct {

	// Output only . Additional information about the annotation
	// specific to the AutoML domain.
	//
	// Types that are assignable to Detail:
	//	*AnnotationPayload_Translation
	//	*AnnotationPayload_Classification
	//	*AnnotationPayload_ImageObjectDetection
	//	*AnnotationPayload_TextExtraction
	//	*AnnotationPayload_TextSentiment
	Detail isAnnotationPayload_Detail `protobuf_oneof:"detail"`
	// Output only . The resource ID of the annotation spec that
	// this annotation pertains to. The annotation spec comes from either an
	// ancestor dataset, or the dataset that was used to train the model in use.
	AnnotationSpecId string `protobuf:"bytes,1,opt,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. The value of
	// [display_name][google.cloud.automl.v1.AnnotationSpec.display_name]
	// when the model was trained. Because this field returns a value at model
	// training time, for different models trained using the same dataset, the
	// returned value could be different as model owner could update the
	// `display_name` between any two model training.
	DisplayName string `protobuf:"bytes,5,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// contains filtered or unexported fields
}

func (*AnnotationPayload) Descriptor() ([]byte, []int)

func (x *AnnotationPayload) GetAnnotationSpecId() string

func (x *AnnotationPayload) GetClassification() *ClassificationAnnotation

func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detail

func (x *AnnotationPayload) GetDisplayName() string

func (x *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotation

func (x *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotation

func (x *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotation

func (x *AnnotationPayload) GetTranslation() *TranslationAnnotation

func (*AnnotationPayload) ProtoMessage()

func (x *AnnotationPayload) ProtoReflect() protoreflect.Message

func (x *AnnotationPayload) Reset()

func (x *AnnotationPayload) String() string

type AnnotationPayload_Classification struct {
	// Annotation details for content or image classification.
	Classification *ClassificationAnnotation `protobuf:"bytes,3,opt,name=classification,proto3,oneof"`
}

type AnnotationPayload_ImageObjectDetection struct {
	// Annotation details for image object detection.
	ImageObjectDetection *ImageObjectDetectionAnnotation `protobuf:"bytes,4,opt,name=image_object_detection,json=imageObjectDetection,proto3,oneof"`
}

type AnnotationPayload_TextExtraction struct {
	// Annotation details for text extraction.
	TextExtraction *TextExtractionAnnotation `protobuf:"bytes,6,opt,name=text_extraction,json=textExtraction,proto3,oneof"`
}

type AnnotationPayload_TextSentiment struct {
	// Annotation details for text sentiment.
	TextSentiment *TextSentimentAnnotation `protobuf:"bytes,7,opt,name=text_sentiment,json=textSentiment,proto3,oneof"`
}

type AnnotationPayload_Translation struct {
	// Annotation details for translation.
	Translation *TranslationAnnotation `protobuf:"bytes,2,opt,name=translation,proto3,oneof"`
}

type AnnotationSpec struct {

	// Output only. Resource name of the annotation spec.
	// Form:
	// 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The name of the annotation spec to show in the interface. The name can be
	// up to 32 characters long and must match the regexp `[a-zA-Z0-9_]+`.
	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. The number of examples in the parent dataset
	// labeled by the annotation spec.
	ExampleCount int32 `protobuf:"varint,9,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	// contains filtered or unexported fields
}

func (*AnnotationSpec) Descriptor() ([]byte, []int)

func (x *AnnotationSpec) GetDisplayName() string

func (x *AnnotationSpec) GetExampleCount() int32

func (x *AnnotationSpec) GetName() string

func (*AnnotationSpec) ProtoMessage()

func (x *AnnotationSpec) ProtoReflect() protoreflect.Message

func (x *AnnotationSpec) Reset()

func (x *AnnotationSpec) String() string

type AutoMlClient interface {
	// Creates a dataset.
	CreateDataset(ctx context.Context, in *CreateDatasetRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Gets a dataset.
	GetDataset(ctx context.Context, in *GetDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Lists datasets in a project.
	ListDatasets(ctx context.Context, in *ListDatasetsRequest, opts ...grpc.CallOption) (*ListDatasetsResponse, error)
	// Updates a dataset.
	UpdateDataset(ctx context.Context, in *UpdateDatasetRequest, opts ...grpc.CallOption) (*Dataset, error)
	// Deletes a dataset and all of its contents.
	// Returns empty response in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteDataset(ctx context.Context, in *DeleteDatasetRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Imports data into a dataset.
	// For Tables this method can only be called on an empty Dataset.
	//
	// For Tables:
	// *   A
	// [schema_inference_version][google.cloud.automl.v1.InputConfig.params]
	//     parameter must be explicitly set.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ImportData(ctx context.Context, in *ImportDataRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Exports dataset's data to the provided output location.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportData(ctx context.Context, in *ExportDataRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Gets an annotation spec.
	GetAnnotationSpec(ctx context.Context, in *GetAnnotationSpecRequest, opts ...grpc.CallOption) (*AnnotationSpec, error)
	// Creates a model.
	// Returns a Model in the [response][google.longrunning.Operation.response]
	// field when it completes.
	// When you create a model, several model evaluations are created for it:
	// a global evaluation, and one evaluation for each annotation spec.
	CreateModel(ctx context.Context, in *CreateModelRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Gets a model.
	GetModel(ctx context.Context, in *GetModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Lists models.
	ListModels(ctx context.Context, in *ListModelsRequest, opts ...grpc.CallOption) (*ListModelsResponse, error)
	// Deletes a model.
	// Returns `google.protobuf.Empty` in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteModel(ctx context.Context, in *DeleteModelRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Updates a model.
	UpdateModel(ctx context.Context, in *UpdateModelRequest, opts ...grpc.CallOption) (*Model, error)
	// Deploys a model. If a model is already deployed, deploying it with the
	// same parameters has no effect. Deploying with different parametrs
	// (as e.g. changing
	// [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
	//  will reset the deployment state without pausing the model's availability.
	//
	// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
	// deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	DeployModel(ctx context.Context, in *DeployModelRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Undeploys a model. If the model is not deployed this method has no effect.
	//
	// Only applicable for Text Classification, Image Object Detection and Tables;
	// all other domains manage deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	UndeployModel(ctx context.Context, in *UndeployModelRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Exports a trained, "export-able", model to a user specified Google Cloud
	// Storage location. A model is considered export-able if and only if it has
	// an export format defined for it in
	// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportModel(ctx context.Context, in *ExportModelRequest, opts ...grpc.CallOption) (*longrunningpb.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(ctx context.Context, in *GetModelEvaluationRequest, opts ...grpc.CallOption) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(ctx context.Context, in *ListModelEvaluationsRequest, opts ...grpc.CallOption) (*ListModelEvaluationsResponse, error)
}

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

type AutoMlServer interface {
	// Creates a dataset.
	CreateDataset(context.Context, *CreateDatasetRequest) (*longrunningpb.Operation, error)
	// Gets a dataset.
	GetDataset(context.Context, *GetDatasetRequest) (*Dataset, error)
	// Lists datasets in a project.
	ListDatasets(context.Context, *ListDatasetsRequest) (*ListDatasetsResponse, error)
	// Updates a dataset.
	UpdateDataset(context.Context, *UpdateDatasetRequest) (*Dataset, error)
	// Deletes a dataset and all of its contents.
	// Returns empty response in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteDataset(context.Context, *DeleteDatasetRequest) (*longrunningpb.Operation, error)
	// Imports data into a dataset.
	// For Tables this method can only be called on an empty Dataset.
	//
	// For Tables:
	// *   A
	// [schema_inference_version][google.cloud.automl.v1.InputConfig.params]
	//     parameter must be explicitly set.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ImportData(context.Context, *ImportDataRequest) (*longrunningpb.Operation, error)
	// Exports dataset's data to the provided output location.
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportData(context.Context, *ExportDataRequest) (*longrunningpb.Operation, error)
	// Gets an annotation spec.
	GetAnnotationSpec(context.Context, *GetAnnotationSpecRequest) (*AnnotationSpec, error)
	// Creates a model.
	// Returns a Model in the [response][google.longrunning.Operation.response]
	// field when it completes.
	// When you create a model, several model evaluations are created for it:
	// a global evaluation, and one evaluation for each annotation spec.
	CreateModel(context.Context, *CreateModelRequest) (*longrunningpb.Operation, error)
	// Gets a model.
	GetModel(context.Context, *GetModelRequest) (*Model, error)
	// Lists models.
	ListModels(context.Context, *ListModelsRequest) (*ListModelsResponse, error)
	// Deletes a model.
	// Returns `google.protobuf.Empty` in the
	// [response][google.longrunning.Operation.response] field when it completes,
	// and `delete_details` in the
	// [metadata][google.longrunning.Operation.metadata] field.
	DeleteModel(context.Context, *DeleteModelRequest) (*longrunningpb.Operation, error)
	// Updates a model.
	UpdateModel(context.Context, *UpdateModelRequest) (*Model, error)
	// Deploys a model. If a model is already deployed, deploying it with the
	// same parameters has no effect. Deploying with different parametrs
	// (as e.g. changing
	// [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
	//  will reset the deployment state without pausing the model's availability.
	//
	// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
	// deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	DeployModel(context.Context, *DeployModelRequest) (*longrunningpb.Operation, error)
	// Undeploys a model. If the model is not deployed this method has no effect.
	//
	// Only applicable for Text Classification, Image Object Detection and Tables;
	// all other domains manage deployment automatically.
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	UndeployModel(context.Context, *UndeployModelRequest) (*longrunningpb.Operation, error)
	// Exports a trained, "export-able", model to a user specified Google Cloud
	// Storage location. A model is considered export-able if and only if it has
	// an export format defined for it in
	// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
	//
	// Returns an empty response in the
	// [response][google.longrunning.Operation.response] field when it completes.
	ExportModel(context.Context, *ExportModelRequest) (*longrunningpb.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(context.Context, *GetModelEvaluationRequest) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(context.Context, *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)
}

type BatchPredictInputConfig struct {

	// The source of the input.
	//
	// Types that are assignable to Source:
	//	*BatchPredictInputConfig_GcsSource
	Source isBatchPredictInputConfig_Source `protobuf_oneof:"source"`
	// contains filtered or unexported fields
}

func (*BatchPredictInputConfig) Descriptor() ([]byte, []int)

func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source

func (*BatchPredictInputConfig) ProtoMessage()

func (x *BatchPredictInputConfig) ProtoReflect() protoreflect.Message

func (x *BatchPredictInputConfig) Reset()

func (x *BatchPredictInputConfig) String() string

type BatchPredictInputConfig_GcsSource struct {
	// Required. The Google Cloud Storage location for the input content.
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

type BatchPredictOperationMetadata struct {

	// Output only. The input config that was given upon starting this
	// batch predict operation.
	InputConfig *BatchPredictInputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// Output only. Information further describing this batch predict's output.
	OutputInfo *BatchPredictOperationMetadata_BatchPredictOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	// contains filtered or unexported fields
}

func (*BatchPredictOperationMetadata) Descriptor() ([]byte, []int)

func (x *BatchPredictOperationMetadata) GetInputConfig() *BatchPredictInputConfig

func (x *BatchPredictOperationMetadata) GetOutputInfo() *BatchPredictOperationMetadata_BatchPredictOutputInfo

func (*BatchPredictOperationMetadata) ProtoMessage()

func (x *BatchPredictOperationMetadata) ProtoReflect() protoreflect.Message

func (x *BatchPredictOperationMetadata) Reset()

func (x *BatchPredictOperationMetadata) String() string

type BatchPredictOperationMetadata_BatchPredictOutputInfo struct {

	// The output location into which prediction output is written.
	//
	// Types that are assignable to OutputLocation:
	//	*BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory
	OutputLocation isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
	// contains filtered or unexported fields
}

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor() ([]byte, []int)

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory() string

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage()

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect() protoreflect.Message

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset()

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) String() string

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory struct {
	// The full path of the Google Cloud Storage directory created, into which
	// the prediction output is written.
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3,oneof"`
}

type BatchPredictOutputConfig struct {

	// The destination of the output.
	//
	// Types that are assignable to Destination:
	//	*BatchPredictOutputConfig_GcsDestination
	Destination isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"`
	// contains filtered or unexported fields
}

func (*BatchPredictOutputConfig) Descriptor() ([]byte, []int)

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination

func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination

func (*BatchPredictOutputConfig) ProtoMessage()

func (x *BatchPredictOutputConfig) ProtoReflect() protoreflect.Message

func (x *BatchPredictOutputConfig) Reset()

func (x *BatchPredictOutputConfig) String() string

type BatchPredictOutputConfig_GcsDestination struct {
	// Required. The Google Cloud Storage location of the directory where the output is to
	// be written to.
	GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

type BatchPredictRequest struct {
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	InputConfig *BatchPredictInputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`

	OutputConfig *BatchPredictOutputConfig `protobuf:"bytes,4,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`

	Params map[string]string "" /* 153 byte string literal not displayed */

}

func (*BatchPredictRequest) Descriptor() ([]byte, []int)

func (x *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfig

func (x *BatchPredictRequest) GetName() string

func (x *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfig

func (x *BatchPredictRequest) GetParams() map[string]string

func (*BatchPredictRequest) ProtoMessage()

func (x *BatchPredictRequest) ProtoReflect() protoreflect.Message

func (x *BatchPredictRequest) Reset()

func (x *BatchPredictRequest) String() string

type BatchPredictResult struct {
	Metadata map[string]string "" /* 157 byte string literal not displayed */

}

func (*BatchPredictResult) Descriptor() ([]byte, []int)

func (x *BatchPredictResult) GetMetadata() map[string]string

func (*BatchPredictResult) ProtoMessage()

func (x *BatchPredictResult) ProtoReflect() protoreflect.Message

func (x *BatchPredictResult) Reset()

func (x *BatchPredictResult) String() string

type BoundingBoxMetricsEntry struct {
	IouThreshold float32 `protobuf:"fixed32,1,opt,name=iou_threshold,json=iouThreshold,proto3" json:"iou_threshold,omitempty"`

	MeanAveragePrecision float32 `protobuf:"fixed32,2,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`

	ConfidenceMetricsEntries []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry "" /* 135 byte string literal not displayed */

}

func (*BoundingBoxMetricsEntry) Descriptor() ([]byte, []int)

func (x *BoundingBoxMetricsEntry) GetConfidenceMetricsEntries() []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry

func (x *BoundingBoxMetricsEntry) GetIouThreshold() float32

func (x *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32

func (*BoundingBoxMetricsEntry) ProtoMessage()

func (x *BoundingBoxMetricsEntry) ProtoReflect() protoreflect.Message

func (x *BoundingBoxMetricsEntry) Reset()

func (x *BoundingBoxMetricsEntry) String() string

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry struct {

	// Output only. The confidence threshold value used to compute the metrics.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Recall under the given confidence threshold.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision under the given confidence threshold.
	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. The harmonic mean of recall and precision.
	F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
	// contains filtered or unexported fields
}

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score() float32

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision() float32

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage()

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset()

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String() string

type BoundingPoly struct {

	// Output only . The bounding polygon normalized vertices.
	NormalizedVertices []*NormalizedVertex `protobuf:"bytes,2,rep,name=normalized_vertices,json=normalizedVertices,proto3" json:"normalized_vertices,omitempty"`
	// contains filtered or unexported fields
}

func (*BoundingPoly) Descriptor() ([]byte, []int)

func (x *BoundingPoly) GetNormalizedVertices() []*NormalizedVertex

func (*BoundingPoly) ProtoMessage()

func (x *BoundingPoly) ProtoReflect() protoreflect.Message

func (x *BoundingPoly) Reset()

func (x *BoundingPoly) String() string

type ClassificationAnnotation struct {

	// Output only. A confidence estimate between 0.0 and 1.0. A higher value
	// means greater confidence that the annotation is positive. If a user
	// approves an annotation as negative or positive, the score value remains
	// unchanged. If a user creates an annotation, the score is 0 for negative or
	// 1 for positive.
	Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
	// contains filtered or unexported fields
}

func (*ClassificationAnnotation) Descriptor() ([]byte, []int)

func (x *ClassificationAnnotation) GetScore() float32

func (*ClassificationAnnotation) ProtoMessage()

func (x *ClassificationAnnotation) ProtoReflect() protoreflect.Message

func (x *ClassificationAnnotation) Reset()

func (x *ClassificationAnnotation) String() string

type ClassificationEvaluationMetrics struct {
	AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`

	AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`

	LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`

	ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry "" /* 129 byte string literal not displayed */

	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`

	AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`

}

func (*ClassificationEvaluationMetrics) Descriptor() ([]byte, []int)

func (x *ClassificationEvaluationMetrics) GetAnnotationSpecId() []string

func (x *ClassificationEvaluationMetrics) GetAuPrc() float32

func (x *ClassificationEvaluationMetrics) GetAuRoc() float32

func (x *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry

func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (x *ClassificationEvaluationMetrics) GetLogLoss() float32

func (*ClassificationEvaluationMetrics) ProtoMessage()

func (x *ClassificationEvaluationMetrics) ProtoReflect() protoreflect.Message

func (x *ClassificationEvaluationMetrics) Reset()

func (x *ClassificationEvaluationMetrics) String() string

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`

	PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`

	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`

	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`

	FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,omitempty"`

	F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`

	RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`

	PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`

	FalsePositiveRateAt1 float32 "" /* 127 byte string literal not displayed */

	F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`

	TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`

	FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`

	FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`

	TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`

}

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int)

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect() protoreflect.Message

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string

type ClassificationEvaluationMetrics_ConfusionMatrix struct {

	// Output only. IDs of the annotation specs used in the confusion matrix.
	// For Tables CLASSIFICATION
	// [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]
	// only list of [annotation_spec_display_name-s][] is populated.
	AnnotationSpecId []string `protobuf:"bytes,1,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// Output only. Display name of the annotation specs used in the confusion
	// matrix, as they were at the moment of the evaluation. For Tables
	// CLASSIFICATION
	// [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type],
	// distinct values of the target column at the moment of the model
	// evaluation are populated here.
	DisplayName []string `protobuf:"bytes,3,rep,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// Output only. Rows in the confusion matrix. The number of rows is equal to
	// the size of `annotation_spec_id`.
	// `row[i].example_count[j]` is the number of examples that have ground
	// truth of the `annotation_spec_id[i]` and are predicted as
	// `annotation_spec_id[j]` by the model being evaluated.
	Row []*ClassificationEvaluationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=row,proto3" json:"row,omitempty"`
	// contains filtered or unexported fields
}

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int)

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []string

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName() []string

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Row

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage()

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect() protoreflect.Message

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset()

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() string

type ClassificationEvaluationMetrics_ConfusionMatrix_Row struct {

	// Output only. Value of the specific cell in the confusion matrix.
	// The number of values each row has (i.e. the length of the row) is equal
	// to the length of the `annotation_spec_id` field or, if that one is not
	// populated, length of the [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
	ExampleCount []int32 `protobuf:"varint,1,rep,packed,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	// contains filtered or unexported fields
}

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int)

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage()

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect() protoreflect.Message

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset()

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string

type ClassificationType int32

const (
	// An un-set value of this enum.
	ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType = 0
	// At most one label is allowed per example.
	ClassificationType_MULTICLASS ClassificationType = 1
	// Multiple labels are allowed for one example.
	ClassificationType_MULTILABEL ClassificationType = 2
)

func (ClassificationType) Descriptor() protoreflect.EnumDescriptor

func (x ClassificationType) Enum() *ClassificationType

func (ClassificationType) EnumDescriptor() ([]byte, []int)

func (x ClassificationType) Number() protoreflect.EnumNumber

func (x ClassificationType) String() string

func (ClassificationType) Type() protoreflect.EnumType

type CreateDatasetOperationMetadata struct {
	// contains filtered or unexported fields
}

func (*CreateDatasetOperationMetadata) Descriptor() ([]byte, []int)

func (*CreateDatasetOperationMetadata) ProtoMessage()

func (x *CreateDatasetOperationMetadata) ProtoReflect() protoreflect.Message

func (x *CreateDatasetOperationMetadata) Reset()

func (x *CreateDatasetOperationMetadata) String() string

type CreateDatasetRequest struct {

	// Required. The resource name of the project to create the dataset for.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. The dataset to create.
	Dataset *Dataset `protobuf:"bytes,2,opt,name=dataset,proto3" json:"dataset,omitempty"`
	// contains filtered or unexported fields
}

func (*CreateDatasetRequest) Descriptor() ([]byte, []int)

func (x *CreateDatasetRequest) GetDataset() *Dataset

func (x *CreateDatasetRequest) GetParent() string

func (*CreateDatasetRequest) ProtoMessage()

func (x *CreateDatasetRequest) ProtoReflect() protoreflect.Message

func (x *CreateDatasetRequest) Reset()

func (x *CreateDatasetRequest) String() string

type CreateModelOperationMetadata struct {
	// contains filtered or unexported fields
}

func (*CreateModelOperationMetadata) Descriptor() ([]byte, []int)

func (*CreateModelOperationMetadata) ProtoMessage()

func (x *CreateModelOperationMetadata) ProtoReflect() protoreflect.Message

func (x *CreateModelOperationMetadata) Reset()

func (x *CreateModelOperationMetadata) String() string

type CreateModelRequest struct {

	// Required. Resource name of the parent project where the model is being created.
	Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
	// Required. The model to create.
	Model *Model `protobuf:"bytes,4,opt,name=model,proto3" json:"model,omitempty"`
	// contains filtered or unexported fields
}

func (*CreateModelRequest) Descriptor() ([]byte, []int)

func (x *CreateModelRequest) GetModel() *Model

func (x *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage()

func (x *CreateModelRequest) ProtoReflect() protoreflect.Message

func (x *CreateModelRequest) Reset()

func (x *CreateModelRequest) String() string

type Dataset struct {
	DatasetMetadata isDataset_DatasetMetadata `protobuf_oneof:"dataset_metadata"`

	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`

	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`

	Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty"`

	ExampleCount int32 `protobuf:"varint,21,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`

	CreateTime *timestamppb.Timestamp `protobuf:"bytes,14,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`

	Etag string `protobuf:"bytes,17,opt,name=etag,proto3" json:"etag,omitempty"`

	Labels map[string]string "" /* 154 byte string literal not displayed */

}

func (*Dataset) Descriptor() ([]byte, []int)

func (x *Dataset) GetCreateTime() *timestamppb.Timestamp

func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadata

func (x *Dataset) GetDescription() string

func (x *Dataset) GetDisplayName() string

func (x *Dataset) GetEtag() string

func (x *Dataset) GetExampleCount() int32

func (x *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadata

func (x *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadata

func (x *Dataset) GetLabels() map[string]string