Documentation

Index

Constants

This section is empty.

Variables

View Source
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,
	}
)

Enum value maps for ClassificationType.

View Source
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,
	}
)

Enum value maps for DocumentDimensions_DocumentDimensionUnit.

View Source
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,
	}
)

Enum value maps for Document_Layout_TextSegmentType.

View Source
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,
	}
)

Enum value maps for Model_DeploymentState.

View Source
var File_google_cloud_automl_v1_annotation_payload_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_annotation_spec_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_classification_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_data_items_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_dataset_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_detection_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_geometry_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_image_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_io_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_model_evaluation_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_model_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_operations_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_prediction_service_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_service_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_text_extraction_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_text_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_text_segment_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_text_sentiment_proto protoreflect.FileDescriptor
View Source
var File_google_cloud_automl_v1_translation_proto protoreflect.FileDescriptor

Functions

func RegisterAutoMlServer

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

func RegisterPredictionServiceServer

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

Types

type AnnotationPayload

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
}

Contains annotation information that is relevant to AutoML.

func (*AnnotationPayload) Descriptor

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

Deprecated: Use AnnotationPayload.ProtoReflect.Descriptor instead.

func (*AnnotationPayload) GetAnnotationSpecId

func (x *AnnotationPayload) GetAnnotationSpecId() string

func (*AnnotationPayload) GetClassification

func (x *AnnotationPayload) GetClassification() *ClassificationAnnotation

func (*AnnotationPayload) GetDetail

func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detail

func (*AnnotationPayload) GetDisplayName

func (x *AnnotationPayload) GetDisplayName() string

func (*AnnotationPayload) GetImageObjectDetection

func (x *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotation

func (*AnnotationPayload) GetTextExtraction

func (x *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotation

func (*AnnotationPayload) GetTextSentiment

func (x *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotation

func (*AnnotationPayload) GetTranslation

func (x *AnnotationPayload) GetTranslation() *TranslationAnnotation

func (*AnnotationPayload) ProtoMessage

func (*AnnotationPayload) ProtoMessage()

func (*AnnotationPayload) ProtoReflect

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

func (*AnnotationPayload) Reset

func (x *AnnotationPayload) Reset()

func (*AnnotationPayload) String

func (x *AnnotationPayload) String() string

type AnnotationPayload_Classification

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

type AnnotationPayload_ImageObjectDetection

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

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

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

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

type AnnotationSpec

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
}

A definition of an annotation spec.

func (*AnnotationSpec) Descriptor

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

Deprecated: Use AnnotationSpec.ProtoReflect.Descriptor instead.

func (*AnnotationSpec) GetDisplayName

func (x *AnnotationSpec) GetDisplayName() string

func (*AnnotationSpec) GetExampleCount

func (x *AnnotationSpec) GetExampleCount() int32

func (*AnnotationSpec) GetName

func (x *AnnotationSpec) GetName() string

func (*AnnotationSpec) ProtoMessage

func (*AnnotationSpec) ProtoMessage()

func (*AnnotationSpec) ProtoReflect

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

func (*AnnotationSpec) Reset

func (x *AnnotationSpec) Reset()

func (*AnnotationSpec) String

func (x *AnnotationSpec) String() string

type AutoMlClient

type AutoMlClient interface {
	// Creates a dataset.
	CreateDataset(ctx context.Context, in *CreateDatasetRequest, opts ...grpc.CallOption) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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)
}

AutoMlClient is the client API for AutoMl service.

For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.

func NewAutoMlClient

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

type AutoMlServer

type AutoMlServer interface {
	// Creates a dataset.
	CreateDataset(context.Context, *CreateDatasetRequest) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.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) (*longrunning.Operation, error)
	// Gets a model evaluation.
	GetModelEvaluation(context.Context, *GetModelEvaluationRequest) (*ModelEvaluation, error)
	// Lists model evaluations.
	ListModelEvaluations(context.Context, *ListModelEvaluationsRequest) (*ListModelEvaluationsResponse, error)
}

AutoMlServer is the server API for AutoMl service.

type BatchPredictInputConfig

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
}

Input configuration for BatchPredict Action.

The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

<h4>AutoML Vision</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5>

One or more CSV files where each line is a single column:

GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

gs://folder/image1.jpeg
gs://folder/image2.gif
gs://folder/image3.png

</section><section><h5>Object Detection</h5>

One or more CSV files where each line is a single column:

GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

  gs://folder/image1.jpeg
  gs://folder/image2.gif
  gs://folder/image3.png
</section>

</div>

<h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5>

One or more CSV files where each line is a single column:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

`GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

`TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the video, and the end time must be after the start time.

Sample rows:

gs://folder/video1.mp4,10,40
gs://folder/video1.mp4,20,60
gs://folder/vid2.mov,0,inf

</section><section><h5>Object Tracking</h5>

One or more CSV files where each line is a single column:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

`GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

`TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the video, and the end time must be after the start time.

Sample rows:

  gs://folder/video1.mp4,10,40
  gs://folder/video1.mp4,20,60
  gs://folder/vid2.mov,0,inf
</section>

</div>

<h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5>

One or more CSV files where each line is a single column:

GCS_FILE_PATH

`GCS_FILE_PATH` is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 10MB in size.

Sample rows:

gs://folder/text1.txt
gs://folder/text2.pdf
gs://folder/text3.tif

</section><section><h5>Sentiment Analysis</h5> One or more CSV files where each line is a single column:

GCS_FILE_PATH

`GCS_FILE_PATH` is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 128kB in size.

Sample rows:

gs://folder/text1.txt
gs://folder/text2.pdf
gs://folder/text3.tif

</section><section><h5>Entity Extraction</h5>

One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict].

Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique.

Each document JSONL file contains, per line, a proto that wraps a Document proto with `input_config` set. Each document cannot exceed 2MB in size.

Supported document extensions: .PDF, .TIF, .TIFF

Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed.

Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".):

{
   "id": "my_first_id",
   "text_snippet": { "content": "dog car cat"},
   "text_features": [
     {
       "text_segment": {"start_offset": 4, "end_offset": 6},
       "structural_type": PARAGRAPH,
       "bounding_poly": {
         "normalized_vertices": [
           {"x": 0.1, "y": 0.1},
           {"x": 0.1, "y": 0.3},
           {"x": 0.3, "y": 0.3},
           {"x": 0.3, "y": 0.1},
         ]
       },
     }
   ],
 }\n
 {
   "id": "2",
   "text_snippet": {
     "content": "Extended sample content",
     "mime_type": "text/plain"
   }
 }

Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".):

   {
     "document": {
       "input_config": {
         "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
         }
       }
     }
   }\n
   {
     "document": {
       "input_config": {
         "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
         }
       }
     }
   }
</section>

</div>

<h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected">

See [Preparing your training data](https://cloud.google.com/automl-tables/docs/predict-batch) for more information.

You can use either [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source] or [bigquery_source][BatchPredictInputConfig.bigquery_source].

**For gcs_source:**

CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

The column names must contain the model's

[input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted.

Sample rows from a CSV file: <pre> "First Name","Last Name","Dob","Addresses"

"John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"

"Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> **For bigquery_source:**

The URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

The column names must contain the model's

[input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows of the table will be attempted.

</section>

</div>

**Input field definitions:**

`GCS_FILE_PATH` : The path to a file on Google Cloud Storage. For example,

"gs://folder/video.avi".

`TIME_SEGMENT_START` : (`TIME_OFFSET`)

Expresses a beginning, inclusive, of a time segment
within an example that has a time dimension
(e.g. video).

`TIME_SEGMENT_END` : (`TIME_OFFSET`)

Expresses an end, exclusive, of a time segment within
n example that has a time dimension (e.g. video).

`TIME_OFFSET` : A number of seconds as measured from the start of an

 example (e.g. video). Fractions are allowed, up to a
 microsecond precision. "inf" is allowed, and it means the end
 of the example.

**Errors:**

If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
prediction does not happen. Regardless of overall success or failure the
per-row failures, up to a certain count cap, will be listed in
Operation.metadata.partial_failures.

func (*BatchPredictInputConfig) Descriptor

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

Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictInputConfig) GetGcsSource

func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource

func (*BatchPredictInputConfig) GetSource

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source

func (*BatchPredictInputConfig) ProtoMessage

func (*BatchPredictInputConfig) ProtoMessage()

func (*BatchPredictInputConfig) ProtoReflect

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

func (*BatchPredictInputConfig) Reset

func (x *BatchPredictInputConfig) Reset()

func (*BatchPredictInputConfig) String

func (x *BatchPredictInputConfig) String() string

type BatchPredictInputConfig_GcsSource

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

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
}

Details of BatchPredict operation.

func (*BatchPredictOperationMetadata) Descriptor

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

Deprecated: Use BatchPredictOperationMetadata.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata) GetInputConfig

func (*BatchPredictOperationMetadata) GetOutputInfo

func (*BatchPredictOperationMetadata) ProtoMessage

func (*BatchPredictOperationMetadata) ProtoMessage()

func (*BatchPredictOperationMetadata) ProtoReflect

func (*BatchPredictOperationMetadata) Reset

func (x *BatchPredictOperationMetadata) Reset()

func (*BatchPredictOperationMetadata) String

type BatchPredictOperationMetadata_BatchPredictOutputInfo

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
}

Further describes this batch predict's output. Supplements

[BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig].

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Descriptor

Deprecated: Use BatchPredictOperationMetadata_BatchPredictOutputInfo.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) String

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory

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

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
}

Output configuration for BatchPredict Action.

As destination the

[gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.

*  For Image Classification:
       In the created directory files `image_classification_1.jsonl`,
       `image_classification_2.jsonl`,...,`image_classification_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of the successfully predicted images and annotations.
       A single image will be listed only once with all its annotations,
       and its annotations will never be split across files.
       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps image's "ID" : "<id_value>" followed by a list of
       zero or more AnnotationPayload protos (called annotations), which
       have classification detail populated.
       If prediction for any image failed (partially or completely), then an
       additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
       files will be created (N depends on total number of failed
       predictions). These files will have a JSON representation of a proto
       that wraps the same "ID" : "<id_value>" but here followed by
       exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

       containing only `code` and `message`fields.

*  For Image Object Detection:
       In the created directory files `image_object_detection_1.jsonl`,
       `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of the successfully predicted images and annotations.
       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps image's "ID" : "<id_value>" followed by a list of
       zero or more AnnotationPayload protos (called annotations), which
       have image_object_detection detail populated. A single image will
       be listed only once with all its annotations, and its annotations
       will never be split across files.
       If prediction for any image failed (partially or completely), then
       additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
       files will be created (N depends on total number of failed
       predictions). These files will have a JSON representation of a proto
       that wraps the same "ID" : "<id_value>" but here followed by
       exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

       containing only `code` and `message`fields.
*  For Video Classification:
       In the created directory a video_classification.csv file, and a .JSON
       file per each video classification requested in the input (i.e. each
       line in given CSV(s)), will be created.

       The format of video_classification.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS

       where:
       GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
           the prediction input lines (i.e. video_classification.csv has
           precisely the same number of lines as the prediction input had.)
       JSON_FILE_NAME = Name of .JSON file in the output directory, which
           contains prediction responses for the video time segment.
       STATUS = "OK" if prediction completed successfully, or an error code
           with message otherwise. If STATUS is not "OK" then the .JSON file
           for that line may not exist or be empty.

       Each .JSON file, assuming STATUS is "OK", will contain a list of
       AnnotationPayload protos in JSON format, which are the predictions
       for the video time segment the file is assigned to in the
       video_classification.csv. All AnnotationPayload protos will have
       video_classification field set, and will be sorted by
       video_classification.type field (note that the returned types are
       governed by `classifaction_types` parameter in
       [PredictService.BatchPredictRequest.params][]).

*  For Video Object Tracking:
       In the created directory a video_object_tracking.csv file will be
       created, and multiple files video_object_trackinng_1.json,
       video_object_trackinng_2.json,..., video_object_trackinng_N.json,
       where N is the number of requests in the input (i.e. the number of
       lines in given CSV(s)).

       The format of video_object_tracking.csv is:

GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS

       where:
       GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
           the prediction input lines (i.e. video_object_tracking.csv has
           precisely the same number of lines as the prediction input had.)
       JSON_FILE_NAME = Name of .JSON file in the output directory, which
           contains prediction responses for the video time segment.
       STATUS = "OK" if prediction completed successfully, or an error
           code with message otherwise. If STATUS is not "OK" then the .JSON
           file for that line may not exist or be empty.

       Each .JSON file, assuming STATUS is "OK", will contain a list of
       AnnotationPayload protos in JSON format, which are the predictions
       for each frame of the video time segment the file is assigned to in
       video_object_tracking.csv. All AnnotationPayload protos will have
       video_object_tracking field set.
*  For Text Classification:
       In the created directory files `text_classification_1.jsonl`,
       `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of inputs and annotations found.

       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps input text file (or document) in
       the text snippet (or document) proto and a list of
       zero or more AnnotationPayload protos (called annotations), which
       have classification detail populated. A single text file (or
       document) will be listed only once with all its annotations, and its
       annotations will never be split across files.

       If prediction for any input file (or document) failed (partially or
       completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
       `errors_N.jsonl` files will be created (N depends on total number of
       failed predictions). These files will have a JSON representation of a
       proto that wraps input file followed by exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

       containing only `code` and `message`.

*  For Text Sentiment:
       In the created directory files `text_sentiment_1.jsonl`,
       `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
       will be created, where N may be 1, and depends on the
       total number of inputs and annotations found.

       Each .JSONL file will contain, per line, a JSON representation of a
       proto that wraps input text file (or document) in
       the text snippet (or document) proto and a list of
       zero or more AnnotationPayload protos (called annotations), which
       have text_sentiment detail populated. A single text file (or
       document) will be listed only once with all its annotations, and its
       annotations will never be split across files.

       If prediction for any input file (or document) failed (partially or
       completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
       `errors_N.jsonl` files will be created (N depends on total number of
       failed predictions). These files will have a JSON representation of a
       proto that wraps input file followed by exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

      containing only `code` and `message`.

*  For Text Extraction:
      In the created directory files `text_extraction_1.jsonl`,
      `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
      will be created, where N may be 1, and depends on the
      total number of inputs and annotations found.
      The contents of these .JSONL file(s) depend on whether the input
      used inline text, or documents.
      If input was inline, then each .JSONL file will contain, per line,
        a JSON representation of a proto that wraps given in request text
        snippet's "id" (if specified), followed by input text snippet,
        and a list of zero or more
        AnnotationPayload protos (called annotations), which have
        text_extraction detail populated. A single text snippet will be
        listed only once with all its annotations, and its annotations will
        never be split across files.
      If input used documents, then each .JSONL file will contain, per
        line, a JSON representation of a proto that wraps given in request
        document proto, followed by its OCR-ed representation in the form
        of a text snippet, finally followed by a list of zero or more
        AnnotationPayload protos (called annotations), which have
        text_extraction detail populated and refer, via their indices, to
        the OCR-ed text snippet. A single document (and its text snippet)
        will be listed only once with all its annotations, and its
        annotations will never be split across files.
      If prediction for any text snippet failed (partially or completely),
      then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
      `errors_N.jsonl` files will be created (N depends on total number of
      failed predictions). These files will have a JSON representation of a
      proto that wraps either the "id" : "<id_value>" (in case of inline)
      or the document proto (in case of document) but here followed by
      exactly one

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

       containing only `code` and `message`.

*  For Tables:
       Output depends on whether

[gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination]

or

[bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination]

is set (either is allowed).
Google Cloud Storage case:
  In the created directory files `tables_1.csv`, `tables_2.csv`,...,
  `tables_N.csv` will be created, where N may be 1, and depends on
  the total number of the successfully predicted rows.
  For all CLASSIFICATION

[prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]:

Each .csv file will contain a header, listing all columns'

[display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name]

given on input followed by M target column names in the format of

"<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>_<target

  value>_score" where M is the number of distinct target values,
  i.e. number of distinct values in the target column of the table
  used to train the model. Subsequent lines will contain the
  respective values of successfully predicted rows, with the last,
  i.e. the target, columns having the corresponding prediction
  [scores][google.cloud.automl.v1p1beta.TablesAnnotation.score].
For REGRESSION and FORECASTING

[prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]:

Each .csv file will contain a header, listing all columns'
[display_name-s][google.cloud.automl.v1p1beta.display_name]
given on input followed by the predicted target column with name
in the format of

"predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>"

Subsequent lines will contain the respective values of
successfully predicted rows, with the last, i.e. the target,
column having the predicted target value.
If prediction for any rows failed, then an additional
`errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be
created (N depends on total number of failed rows). These files
will have analogous format as `tables_*.csv`, but always with a
single target column having

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

    represented as a JSON string, and containing only `code` and
    `message`.
BigQuery case:

[bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination]

pointing to a BigQuery project must be set. In the given project a
new dataset will be created with name
`prediction_<model-display-name>_<timestamp-of-prediction-call>`
where <model-display-name> will be made
BigQuery-dataset-name compatible (e.g. most special characters will
become underscores), and timestamp will be in
YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
two tables will be created, `predictions`, and `errors`.
The `predictions` table's column names will be the input columns'

[display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name]

followed by the target column with name in the format of

"predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>"

The input feature columns will contain the respective values of
successfully predicted rows, with the target column having an
ARRAY of

[AnnotationPayloads][google.cloud.automl.v1p1beta.AnnotationPayload],

represented as STRUCT-s, containing
[TablesAnnotation][google.cloud.automl.v1p1beta.TablesAnnotation].
The `errors` table contains rows for which the prediction has
failed, it has analogous input columns while the target column name
is in the format of

"errors_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]

[display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>",

and as a value has

[`google.rpc.Status`](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)

represented as a STRUCT, and containing only `code` and `message`.

func (*BatchPredictOutputConfig) Descriptor

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

Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictOutputConfig) GetDestination

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination

func (*BatchPredictOutputConfig) GetGcsDestination

func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination

func (*BatchPredictOutputConfig) ProtoMessage

func (*BatchPredictOutputConfig) ProtoMessage()

func (*BatchPredictOutputConfig) ProtoReflect

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

func (*BatchPredictOutputConfig) Reset

func (x *BatchPredictOutputConfig) Reset()

func (*BatchPredictOutputConfig) String

func (x *BatchPredictOutputConfig) String() string

type BatchPredictOutputConfig_GcsDestination

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

type BatchPredictRequest struct {

	// Required. Name of the model requested to serve the batch prediction.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The input configuration for batch prediction.
	InputConfig *BatchPredictInputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// Required. The Configuration specifying where output predictions should
	// be written.
	OutputConfig *BatchPredictOutputConfig `protobuf:"bytes,4,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	// Additional domain-specific parameters for the predictions, any string must
	// be up to 25000 characters long.
	//
	// AutoML Natural Language Classification
	//
	// `score_threshold`
	// : (float) A value from 0.0 to 1.0. When the model
	//   makes predictions for a text snippet, it will only produce results
	//   that have at least this confidence score. The default is 0.5.
	//
	//
	// AutoML Vision Classification
	//
	// `score_threshold`
	// : (float) A value from 0.0 to 1.0. When the model
	//   makes predictions for an image, it will only produce results that
	//   have at least this confidence score. The default is 0.5.
	//
	// AutoML Vision Object Detection
	//
	// `score_threshold`
	// : (float) When Model detects objects on the image,
	//   it will only produce bounding boxes which have at least this
	//   confidence score. Value in 0 to 1 range, default is 0.5.
	//
	// `max_bounding_box_count`
	// : (int64) The maximum number of bounding
	//   boxes returned per image. The default is 100, the
	//   number of bounding boxes returned might be limited by the server.
	// AutoML Video Intelligence Classification
	//
	// `score_threshold`
	// : (float) A value from 0.0 to 1.0. When the model
	//   makes predictions for a video, it will only produce results that
	//   have at least this confidence score. The default is 0.5.
	//
	// `segment_classification`
	// : (boolean) Set to true to request
	//   segment-level classification. AutoML Video Intelligence returns
	//   labels and their confidence scores for the entire segment of the
	//   video that user specified in the request configuration.
	//   The default is true.
	//
	// `shot_classification`
	// : (boolean) Set to true to request shot-level
	//   classification. AutoML Video Intelligence determines the boundaries
	//   for each camera shot in the entire segment of the video that user
	//   specified in the request configuration. AutoML Video Intelligence
	//   then returns labels and their confidence scores for each detected
	//   shot, along with the start and end time of the shot.
	//   The default is false.
	//
	//   WARNING: Model evaluation is not done for this classification type,
	//   the quality of it depends on training data, but there are no metrics
	//   provided to describe that quality.
	//
	// `1s_interval_classification`
	// : (boolean) Set to true to request
	//   classification for a video at one-second intervals. AutoML Video
	//   Intelligence returns labels and their confidence scores for each
	//   second of the entire segment of the video that user specified in the
	//   request configuration. The default is false.
	//
	//   WARNING: Model evaluation is not done for this classification
	//   type, the quality of it depends on training data, but there are no
	//   metrics provided to describe that quality.
	//
	// AutoML Video Intelligence Object Tracking
	//
	// `score_threshold`
	// : (float) When Model detects objects on video frames,
	//   it will only produce bounding boxes which have at least this
	//   confidence score. Value in 0 to 1 range, default is 0.5.
	//
	// `max_bounding_box_count`
	// : (int64) The maximum number of bounding
	//   boxes returned per image. The default is 100, the
	//   number of bounding boxes returned might be limited by the server.
	//
	// `min_bounding_box_size`
	// : (float) Only bounding boxes with shortest edge
	//   at least that long as a relative value of video frame size are
	//   returned. Value in 0 to 1 range. Default is 0.
	//
	Params map[string]string `` /* 153-byte string literal not displayed */
	// contains filtered or unexported fields
}

Request message for [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].

func (*BatchPredictRequest) Descriptor

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

Deprecated: Use BatchPredictRequest.ProtoReflect.Descriptor instead.

func (*BatchPredictRequest) GetInputConfig

func (x *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictRequest) GetName

func (x *BatchPredictRequest) GetName() string

func (*BatchPredictRequest) GetOutputConfig

func (x *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfig

func (*BatchPredictRequest) GetParams

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

func (*BatchPredictRequest) ProtoMessage

func (*BatchPredictRequest) ProtoMessage()

func (*BatchPredictRequest) ProtoReflect

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

func (*BatchPredictRequest) Reset

func (x *BatchPredictRequest) Reset()

func (*BatchPredictRequest) String

func (x *BatchPredictRequest) String() string

type BatchPredictResult

type BatchPredictResult struct {

	// Additional domain-specific prediction response metadata.
	//
	// AutoML Vision Object Detection
	//
	// `max_bounding_box_count`
	// : (int64) The maximum number of bounding boxes returned per image.
	//
	// AutoML Video Intelligence Object Tracking
	//
	// `max_bounding_box_count`
	// : (int64) The maximum number of bounding boxes returned per frame.
	Metadata map[string]string `` /* 157-byte string literal not displayed */
	// contains filtered or unexported fields
}

Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].

func (*BatchPredictResult) Descriptor

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

Deprecated: Use BatchPredictResult.ProtoReflect.Descriptor instead.

func (*BatchPredictResult) GetMetadata

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

func (*BatchPredictResult) ProtoMessage

func (*BatchPredictResult) ProtoMessage()

func (*BatchPredictResult) ProtoReflect

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

func (*BatchPredictResult) Reset

func (x *BatchPredictResult) Reset()

func (*BatchPredictResult) String

func (x *BatchPredictResult) String() string

type BoundingBoxMetricsEntry

type BoundingBoxMetricsEntry struct {

	// Output only. The intersection-over-union threshold value used to compute
	// this metrics entry.
	IouThreshold float32 `protobuf:"fixed32,1,opt,name=iou_threshold,json=iouThreshold,proto3" json:"iou_threshold,omitempty"`
	// Output only. The mean average precision, most often close to au_prc.
	MeanAveragePrecision float32 `protobuf:"fixed32,2,opt,name=mean_average_precision,json=meanAveragePrecision,proto3" json:"mean_average_precision,omitempty"`
	// Output only. Metrics for each label-match confidence_threshold from
	// 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is
	// derived from them.
	ConfidenceMetricsEntries []*BoundingBoxMetricsEntry_ConfidenceMetricsEntry `` /* 135-byte string literal not displayed */
	// contains filtered or unexported fields
}

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

func (*BoundingBoxMetricsEntry) Descriptor

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

Deprecated: Use BoundingBoxMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry) GetConfidenceMetricsEntries

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

func (*BoundingBoxMetricsEntry) GetIouThreshold

func (x *BoundingBoxMetricsEntry) GetIouThreshold() float32

func (*BoundingBoxMetricsEntry) GetMeanAveragePrecision

func (x *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32

func (*BoundingBoxMetricsEntry) ProtoMessage

func (*BoundingBoxMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry) ProtoReflect

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

func (*BoundingBoxMetricsEntry) Reset

func (x *BoundingBoxMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry) String

func (x *BoundingBoxMetricsEntry) String() string

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry

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
}

Metrics for a single confidence threshold.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Descriptor

Deprecated: Use BoundingBoxMetricsEntry_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String

type BoundingPoly

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
}

A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.

func (*BoundingPoly) Descriptor

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

Deprecated: Use BoundingPoly.ProtoReflect.Descriptor instead.

func (*BoundingPoly) GetNormalizedVertices

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

func (*BoundingPoly) ProtoMessage

func (*BoundingPoly) ProtoMessage()

func (*BoundingPoly) ProtoReflect

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

func (*BoundingPoly) Reset

func (x *BoundingPoly) Reset()

func (*BoundingPoly) String

func (x *BoundingPoly) String() string

type ClassificationAnnotation

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
}

Contains annotation details specific to classification.

func (*ClassificationAnnotation) Descriptor

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

Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead.

func (*ClassificationAnnotation) GetScore

func (x *ClassificationAnnotation) GetScore() float32

func (*ClassificationAnnotation) ProtoMessage

func (*ClassificationAnnotation) ProtoMessage()

func (*ClassificationAnnotation) ProtoReflect

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

func (*ClassificationAnnotation) Reset

func (x *ClassificationAnnotation) Reset()

func (*ClassificationAnnotation) String

func (x *ClassificationAnnotation) String() string

type ClassificationEvaluationMetrics

type ClassificationEvaluationMetrics struct {

	// Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
	// for the overall evaluation.
	AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
	// Output only. The Area Under Receiver Operating Characteristic curve metric.
	// Micro-averaged for the overall evaluation.
	AuRoc float32 `protobuf:"fixed32,6,opt,name=au_roc,json=auRoc,proto3" json:"au_roc,omitempty"`
	// Output only. The Log Loss metric.
	LogLoss float32 `protobuf:"fixed32,7,opt,name=log_loss,json=logLoss,proto3" json:"log_loss,omitempty"`
	// Output only. Metrics for each confidence_threshold in
	// 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
	// position_threshold = INT32_MAX_VALUE.
	// ROC and precision-recall curves, and other aggregated metrics are derived
	// from them. The confidence metrics entries may also be supplied for
	// additional values of position_threshold, but from these no aggregated
	// metrics are computed.
	ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry `` /* 129-byte string literal not displayed */
	// Output only. Confusion matrix of the evaluation.
	// Only set for MULTICLASS classification problems where number
	// of labels is no more than 10.
	// Only set for model level evaluation, not for evaluation per label.
	ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"`
	// Output only. The annotation spec ids used for this evaluation.
	AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"`
	// contains filtered or unexported fields
}

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

func (*ClassificationEvaluationMetrics) Descriptor

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

Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics) GetAnnotationSpecId

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

func (*ClassificationEvaluationMetrics) GetAuPrc

func (*ClassificationEvaluationMetrics) GetAuRoc

func (*ClassificationEvaluationMetrics) GetConfidenceMetricsEntry

func (*ClassificationEvaluationMetrics) GetConfusionMatrix

func (*ClassificationEvaluationMetrics) GetLogLoss

func (x *ClassificationEvaluationMetrics) GetLogLoss() float32

func (*ClassificationEvaluationMetrics) ProtoMessage

func (*ClassificationEvaluationMetrics) ProtoMessage()

func (*ClassificationEvaluationMetrics) ProtoReflect

func (*ClassificationEvaluationMetrics) Reset

func (*ClassificationEvaluationMetrics) String

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct {

	// Output only. Metrics are computed with an assumption that the model
	// never returns predictions with score lower than this value.
	ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"`
	// Output only. Metrics are computed with an assumption that the model
	// always returns at most this many predictions (ordered by their score,
	// descendingly), but they all still need to meet the confidence_threshold.
	PositionThreshold int32 `protobuf:"varint,14,opt,name=position_threshold,json=positionThreshold,proto3" json:"position_threshold,omitempty"`
	// Output only. Recall (True Positive Rate) for the given confidence
	// threshold.
	Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"`
	// Output only. Precision for the given confidence threshold.
	Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"`
	// Output only. False Positive Rate for the given confidence threshold.
	FalsePositiveRate float32 `protobuf:"fixed32,8,opt,name=false_positive_rate,json=falsePositiveRate,proto3" json:"false_positive_rate,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"`
	// Output only. The Recall (True Positive Rate) when only considering the
	// label that has the highest prediction score and not below the confidence
	// threshold for each example.
	RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"`
	// Output only. The precision when only considering the label that has the
	// highest prediction score and not below the confidence threshold for each
	// example.
	PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"`
	// Output only. The False Positive Rate when only considering the label that
	// has the highest prediction score and not below the confidence threshold
	// for each example.
	FalsePositiveRateAt1 float32 `` /* 127-byte string literal not displayed */
	// Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
	F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"`
	// Output only. The number of model created labels that match a ground truth
	// label.
	TruePositiveCount int64 `protobuf:"varint,10,opt,name=true_positive_count,json=truePositiveCount,proto3" json:"true_positive_count,omitempty"`
	// Output only. The number of model created labels that do not match a
	// ground truth label.
	FalsePositiveCount int64 `protobuf:"varint,11,opt,name=false_positive_count,json=falsePositiveCount,proto3" json:"false_positive_count,omitempty"`
	// Output only. The number of ground truth labels that are not matched
	// by a model created label.
	FalseNegativeCount int64 `protobuf:"varint,12,opt,name=false_negative_count,json=falseNegativeCount,proto3" json:"false_negative_count,omitempty"`
	// Output only. The number of labels that were not created by the model,
	// but if they would, they would not match a ground truth label.
	TrueNegativeCount int64 `protobuf:"varint,13,opt,name=true_negative_count,json=trueNegativeCount,proto3" json:"true_negative_count,omitempty"`
	// contains filtered or unexported fields
}

Metrics for a single confidence threshold.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor

Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String

type ClassificationEvaluationMetrics_ConfusionMatrix

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
}

Confusion matrix of the model running the classification.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetRow

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Reset

func (*ClassificationEvaluationMetrics_ConfusionMatrix) String

type ClassificationEvaluationMetrics_ConfusionMatrix_Row

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
}

Output only. A row in the confusion matrix.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) String

type ClassificationType

type ClassificationType int32

Type of the classification problem.

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

func (ClassificationType) Enum

func (ClassificationType) EnumDescriptor

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

Deprecated: Use ClassificationType.Descriptor instead.

func (ClassificationType) Number

func (ClassificationType) String

func (x ClassificationType) String() string

func (ClassificationType) Type

type CreateDatasetOperationMetadata

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

Details of CreateDataset operation.

func (*CreateDatasetOperationMetadata) Descriptor

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

Deprecated: Use CreateDatasetOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateDatasetOperationMetadata) ProtoMessage

func (*CreateDatasetOperationMetadata) ProtoMessage()

func (*CreateDatasetOperationMetadata) ProtoReflect

func (*CreateDatasetOperationMetadata) Reset

func (x *CreateDatasetOperationMetadata) Reset()

func (*CreateDatasetOperationMetadata) String

type CreateDatasetRequest

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
}

Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].

func (*CreateDatasetRequest) Descriptor

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

Deprecated: Use CreateDatasetRequest.ProtoReflect.Descriptor instead.

func (*CreateDatasetRequest) GetDataset

func (x *CreateDatasetRequest) GetDataset() *Dataset

func (*CreateDatasetRequest) GetParent

func (x *CreateDatasetRequest) GetParent() string

func (*CreateDatasetRequest) ProtoMessage

func (*CreateDatasetRequest) ProtoMessage()

func (*CreateDatasetRequest) ProtoReflect

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

func (*CreateDatasetRequest) Reset

func (x *CreateDatasetRequest) Reset()

func (*CreateDatasetRequest) String

func (x *CreateDatasetRequest) String() string

type CreateModelOperationMetadata

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

Details of CreateModel operation.

func (*CreateModelOperationMetadata) Descriptor

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

Deprecated: Use CreateModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateModelOperationMetadata) ProtoMessage

func (*CreateModelOperationMetadata) ProtoMessage()

func (*CreateModelOperationMetadata) ProtoReflect

func (*CreateModelOperationMetadata) Reset

func (x *CreateModelOperationMetadata) Reset()

func (*CreateModelOperationMetadata) String

type CreateModelRequest

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
}

Request message for [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].

func (*CreateModelRequest) Descriptor

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

Deprecated: Use CreateModelRequest.ProtoReflect.Descriptor instead.

func (*CreateModelRequest) GetModel

func (x *CreateModelRequest) GetModel() *Model

func (*CreateModelRequest) GetParent

func (x *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage

func (*CreateModelRequest) ProtoMessage()

func (*CreateModelRequest) ProtoReflect

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

func (*CreateModelRequest) Reset

func (x *CreateModelRequest) Reset()

func (*CreateModelRequest) String

func (x *CreateModelRequest) String() string

type Dataset

type Dataset struct {

	// Required.
	// The dataset metadata that is specific to the problem type.
	//
	// Types that are assignable to DatasetMetadata:
	//	*Dataset_TranslationDatasetMetadata
	//	*Dataset_ImageClassificationDatasetMetadata
	//	*Dataset_TextClassificationDatasetMetadata
	//	*Dataset_ImageObjectDetectionDatasetMetadata
	//	*Dataset_TextExtractionDatasetMetadata
	//	*Dataset_TextSentimentDatasetMetadata
	DatasetMetadata isDataset_DatasetMetadata `protobuf_oneof:"dataset_metadata"`
	// Output only. The resource name of the dataset.
	// Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The name of the dataset to show in the interface. The name can be
	// up to 32 characters long and can consist only of ASCII Latin letters A-Z
	// and a-z, underscores
	// (_), and ASCII digits 0-9.
	DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
	// User-provided description of the dataset. The description can be up to
	// 25000 characters long.
	Description string `protobuf:"bytes,3,opt,name=description,proto3" json:"description,omitempty"`
	// Output only. The number of examples in the dataset.
	ExampleCount int32 `protobuf:"varint,21,opt,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"`
	// Output only. Timestamp when this dataset was created.
	CreateTime *timestamppb.Timestamp `protobuf:"bytes,14,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
	// Used to perform consistent read-modify-write updates. If not set, a blind
	// "overwrite" update happens.
	Etag string `protobuf:"bytes,17,opt,name=etag,proto3" json:"etag,omitempty"`
	// Optional. The labels with user-defined metadata to organize your dataset.
	//
	// 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.
	// Label values are optional. Label keys must start with a letter.
	//
	// See https://goo.gl/xmQnxf for more information on and examples of labels.
	Labels map[string]string `` /* 154-byte string literal not displayed */
	// contains filtered or unexported fields
}

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

func (*Dataset) Descriptor

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

Deprecated: Use Dataset.ProtoReflect.Descriptor instead.

func (*Dataset) GetCreateTime

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

func (*Dataset) GetDatasetMetadata

func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadata

func (*Dataset) GetDescription

func (x *Dataset) GetDescription() string

func (*Dataset) GetDisplayName

func (x *Dataset) GetDisplayName() string

func (*Dataset) GetEtag

func (x *Dataset) GetEtag() string

func (*Dataset) GetExampleCount

func (x *Dataset) GetExampleCount() int32

func (*Dataset) GetImageClassificationDatasetMetadata

func (x *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadata

func (*Dataset) GetImageObjectDetectionDatasetMetadata

func (x *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadata

func (*Dataset) GetLabels

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

func (*Dataset) GetName

func (x *Dataset) GetName() string

func (*Dataset) GetTextClassificationDatasetMetadata

func (x *Dataset) GetTextClassificationDatasetMetadata() *TextClassificationDatasetMetadata

func (*Dataset) GetTextExtractionDatasetMetadata

func (x *Dataset) GetTextExtractionDatasetMetadata() *TextExtractionDatasetMetadata

func (*Dataset) GetTextSentimentDatasetMetadata

func (x *Dataset) GetTextSentimentDatasetMetadata() *TextSentimentDatasetMetadata

func (*Dataset) GetTranslationDatasetMetadata

func (x *Dataset) GetTranslationDatasetMetadata() *TranslationDatasetMetadata

func (*Dataset) ProtoMessage

func (*Dataset) ProtoMessage()

func (*Dataset) ProtoReflect

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

func (*Dataset) Reset

func (x *Dataset) Reset()

func (*Dataset) String

func (x *Dataset) String() string

type Dataset_ImageClassificationDatasetMetadata

type Dataset_ImageClassificationDatasetMetadata struct {
	// Metadata for a dataset used for image classification.
	ImageClassificationDatasetMetadata *ImageClassificationDatasetMetadata `protobuf:"bytes,24,opt,name=image_classification_dataset_metadata,json=imageClassificationDatasetMetadata,proto3,oneof"`
}

type Dataset_ImageObjectDetectionDatasetMetadata

type Dataset_ImageObjectDetectionDatasetMetadata struct {
	// Metadata for a dataset used for image object detection.
	ImageObjectDetectionDatasetMetadata *ImageObjectDetectionDatasetMetadata `protobuf:"bytes,26,opt,name=image_object_detection_dataset_metadata,json=imageObjectDetectionDatasetMetadata,proto3,oneof"`
}

type Dataset_TextClassificationDatasetMetadata

type Dataset_TextClassificationDatasetMetadata struct {
	// Metadata for a dataset used for text classification.
	TextClassificationDatasetMetadata *TextClassificationDatasetMetadata `protobuf:"bytes,25,opt,name=text_classification_dataset_metadata,json=textClassificationDatasetMetadata,proto3,oneof"`
}

type Dataset_TextExtractionDatasetMetadata

type Dataset_TextExtractionDatasetMetadata struct {
	// Metadata for a dataset used for text extraction.
	TextExtractionDatasetMetadata *TextExtractionDatasetMetadata `protobuf:"bytes,28,opt,name=text_extraction_dataset_metadata,json=textExtractionDatasetMetadata,proto3,oneof"`
}

type Dataset_TextSentimentDatasetMetadata

type Dataset_TextSentimentDatasetMetadata struct {
	// Metadata for a dataset used for text sentiment.
	TextSentimentDatasetMetadata *TextSentimentDatasetMetadata `protobuf:"bytes,30,opt,name=text_sentiment_dataset_metadata,json=textSentimentDatasetMetadata,proto3,oneof"`
}

type Dataset_TranslationDatasetMetadata

type Dataset_TranslationDatasetMetadata struct {
	// Metadata for a dataset used for translation.
	TranslationDatasetMetadata *TranslationDatasetMetadata `protobuf:"bytes,23,opt,name=translation_dataset_metadata,json=translationDatasetMetadata,proto3,oneof"`
}

type DeleteDatasetRequest

type DeleteDatasetRequest struct {

	// Required. The resource name of the dataset to delete.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].

func (*DeleteDatasetRequest) Descriptor

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

Deprecated: Use DeleteDatasetRequest.ProtoReflect.Descriptor instead.

func (*DeleteDatasetRequest) GetName

func (x *DeleteDatasetRequest) GetName() string

func (*DeleteDatasetRequest) ProtoMessage

func (*DeleteDatasetRequest) ProtoMessage()

func (*DeleteDatasetRequest) ProtoReflect

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

func (*DeleteDatasetRequest) Reset

func (x *DeleteDatasetRequest) Reset()

func (*DeleteDatasetRequest) String

func (x *DeleteDatasetRequest) String() string

type DeleteModelRequest

type DeleteModelRequest struct {

	// Required. Resource name of the model being deleted.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].

func (*DeleteModelRequest) Descriptor

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

Deprecated: Use DeleteModelRequest.ProtoReflect.Descriptor instead.

func (*DeleteModelRequest) GetName

func (x *DeleteModelRequest) GetName() string

func (*DeleteModelRequest) ProtoMessage

func (*DeleteModelRequest) ProtoMessage()

func (*DeleteModelRequest) ProtoReflect

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

func (*DeleteModelRequest) Reset

func (x *DeleteModelRequest) Reset()

func (*DeleteModelRequest) String

func (x *DeleteModelRequest) String() string

type DeleteOperationMetadata

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

Details of operations that perform deletes of any entities.

func (*DeleteOperationMetadata) Descriptor

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

Deprecated: Use DeleteOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeleteOperationMetadata) ProtoMessage

func (*DeleteOperationMetadata) ProtoMessage()

func (*DeleteOperationMetadata) ProtoReflect

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

func (*DeleteOperationMetadata) Reset

func (x *DeleteOperationMetadata) Reset()

func (*DeleteOperationMetadata) String

func (x *DeleteOperationMetadata) String() string

type DeployModelOperationMetadata

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

Details of DeployModel operation.

func (*DeployModelOperationMetadata) Descriptor

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

Deprecated: Use DeployModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeployModelOperationMetadata) ProtoMessage

func (*DeployModelOperationMetadata) ProtoMessage()

func (*DeployModelOperationMetadata) ProtoReflect

func (*DeployModelOperationMetadata) Reset

func (x *DeployModelOperationMetadata) Reset()

func (*DeployModelOperationMetadata) String

type DeployModelRequest

type DeployModelRequest struct {

	// The per-domain specific deployment parameters.
	//
	// Types that are assignable to ModelDeploymentMetadata:
	//	*DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata
	//	*DeployModelRequest_ImageClassificationModelDeploymentMetadata
	ModelDeploymentMetadata isDeployModelRequest_ModelDeploymentMetadata `protobuf_oneof:"model_deployment_metadata"`
	// Required. Resource name of the model to deploy.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].

func (*DeployModelRequest) Descriptor

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

Deprecated: Use DeployModelRequest.ProtoReflect.Descriptor instead.

func (*DeployModelRequest) GetImageClassificationModelDeploymentMetadata

func (x *DeployModelRequest) GetImageClassificationModelDeploymentMetadata() *ImageClassificationModelDeploymentMetadata

func (*DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata

func (x *DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata() *ImageObjectDetectionModelDeploymentMetadata

func (*DeployModelRequest) GetModelDeploymentMetadata

func (m *DeployModelRequest) GetModelDeploymentMetadata() isDeployModelRequest_ModelDeploymentMetadata

func (*DeployModelRequest) GetName

func (x *DeployModelRequest) GetName() string

func (*DeployModelRequest) ProtoMessage

func (*DeployModelRequest) ProtoMessage()

func (*DeployModelRequest) ProtoReflect

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

func (*DeployModelRequest) Reset

func (x *DeployModelRequest) Reset()

func (*DeployModelRequest) String

func (x *DeployModelRequest) String() string

type DeployModelRequest_ImageClassificationModelDeploymentMetadata

type DeployModelRequest_ImageClassificationModelDeploymentMetadata struct {
	// Model deployment metadata specific to Image Classification.
	ImageClassificationModelDeploymentMetadata *ImageClassificationModelDeploymentMetadata `` /* 135-byte string literal not displayed */
}

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata struct {
	// Model deployment metadata specific to Image Object Detection.
	ImageObjectDetectionModelDeploymentMetadata *ImageObjectDetectionModelDeploymentMetadata `` /* 138-byte string literal not displayed */
}

type Document

type Document struct {

	// An input config specifying the content of the document.
	InputConfig *DocumentInputConfig `protobuf:"bytes,1,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// The plain text version of this document.
	DocumentText *TextSnippet `protobuf:"bytes,2,opt,name=document_text,json=documentText,proto3" json:"document_text,omitempty"`
	// Describes the layout of the document.
	// Sorted by [page_number][].
	Layout []*Document_Layout `protobuf:"bytes,3,rep,name=layout,proto3" json:"layout,omitempty"`
	// The dimensions of the page in the document.
	DocumentDimensions *DocumentDimensions `protobuf:"bytes,4,opt,name=document_dimensions,json=documentDimensions,proto3" json:"document_dimensions,omitempty"`
	// Number of pages in the document.
	PageCount int32 `protobuf:"varint,5,opt,name=page_count,json=pageCount,proto3" json:"page_count,omitempty"`
	// contains filtered or unexported fields
}

A structured text document e.g. a PDF.

func (*Document) Descriptor

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

Deprecated: Use Document.ProtoReflect.Descriptor instead.

func (*Document) GetDocumentDimensions

func (x *Document) GetDocumentDimensions() *DocumentDimensions

func (*Document) GetDocumentText

func (x *Document) GetDocumentText() *TextSnippet

func (*Document) GetInputConfig

func (x *Document) GetInputConfig() *DocumentInputConfig

func (*Document) GetLayout

func (x *Document) GetLayout() []*Document_Layout

func (*Document) GetPageCount

func (x *Document) GetPageCount() int32

func (*Document) ProtoMessage

func (*Document) ProtoMessage()

func (*Document) ProtoReflect

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

func (*Document) Reset

func (x *Document) Reset()

func (*Document) String

func (x *Document) String() string

type DocumentDimensions

type DocumentDimensions struct {

	// Unit of the dimension.
	Unit DocumentDimensions_DocumentDimensionUnit `` /* 131-byte string literal not displayed */
	// Width value of the document, works together with the unit.
	Width float32 `protobuf:"fixed32,2,opt,name=width,proto3" json:"width,omitempty"`
	// Height value of the document, works together with the unit.
	Height float32 `protobuf:"fixed32,3,opt,name=height,proto3" json:"height,omitempty"`
	// contains filtered or unexported fields
}

Message that describes dimension of a document.

func (*DocumentDimensions) Descriptor

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

Deprecated: Use DocumentDimensions.ProtoReflect.Descriptor instead.

func (*DocumentDimensions) GetHeight

func (x *DocumentDimensions) GetHeight() float32

func (*DocumentDimensions) GetUnit

func (*DocumentDimensions) GetWidth

func (x *DocumentDimensions) GetWidth() float32

func (*DocumentDimensions) ProtoMessage

func (*DocumentDimensions) ProtoMessage()

func (*DocumentDimensions) ProtoReflect

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

func (*DocumentDimensions) Reset

func (x *DocumentDimensions) Reset()

func (*DocumentDimensions) String

func (x *DocumentDimensions) String() string

type DocumentDimensions_DocumentDimensionUnit

type DocumentDimensions_DocumentDimensionUnit int32

Unit of the document dimension.

const (
	// Should not be used.
	DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED DocumentDimensions_DocumentDimensionUnit = 0
	// Document dimension is measured in inches.
	DocumentDimensions_INCH DocumentDimensions_DocumentDimensionUnit = 1
	// Document dimension is measured in centimeters.
	DocumentDimensions_CENTIMETER DocumentDimensions_DocumentDimensionUnit = 2
	// Document dimension is measured in points. 72 points = 1 inch.
	DocumentDimensions_POINT DocumentDimensions_DocumentDimensionUnit = 3
)

func (DocumentDimensions_DocumentDimensionUnit) Descriptor

func (DocumentDimensions_DocumentDimensionUnit) Enum

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor

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

Deprecated: Use DocumentDimensions_DocumentDimensionUnit.Descriptor instead.

func (DocumentDimensions_DocumentDimensionUnit) Number

func (DocumentDimensions_DocumentDimensionUnit) String

func (DocumentDimensions_DocumentDimensionUnit) Type

type DocumentInputConfig

type DocumentInputConfig struct {

	// The Google Cloud Storage location of the document file. Only a single path
	// should be given.
	//
	// Max supported size: 512MB.
	//
	// Supported extensions: .PDF.
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"`
	// contains filtered or unexported fields
}

Input configuration of a [Document][google.cloud.automl.v1.Document].

func (*DocumentInputConfig) Descriptor

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

Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead.

func (*DocumentInputConfig) GetGcsSource

func (x *DocumentInputConfig) GetGcsSource() *GcsSource

func (*DocumentInputConfig) ProtoMessage

func (*DocumentInputConfig) ProtoMessage()

func (*DocumentInputConfig) ProtoReflect

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

func (*DocumentInputConfig) Reset

func (x *DocumentInputConfig) Reset()

func (*DocumentInputConfig) String

func (x *DocumentInputConfig) String() string

type Document_Layout

type Document_Layout struct {

	// Text Segment that represents a segment in
	// [document_text][google.cloud.automl.v1p1beta.Document.document_text].
	TextSegment *TextSegment `protobuf:"bytes,1,opt,name=text_segment,json=textSegment,proto3" json:"text_segment,omitempty"`
	// Page number of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the original document, starts
	// from 1.
	PageNumber int32 `protobuf:"varint,2,opt,name=page_number,json=pageNumber,proto3" json:"page_number,omitempty"`
	// The position of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the page.
	// Contains exactly 4
	//
	// [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices]
	// and they are connected by edges in the order provided, which will
	// represent a rectangle parallel to the frame. The
	// [NormalizedVertex-s][google.cloud.automl.v1p1beta.NormalizedVertex] are
	// relative to the page.
	// Coordinates are based on top-left as point (0,0).
	BoundingPoly *BoundingPoly `protobuf:"bytes,3,opt,name=bounding_poly,json=boundingPoly,proto3" json:"bounding_poly,omitempty"`
	// The type of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in document.
	TextSegmentType Document_Layout_TextSegmentType `` /* 169-byte string literal not displayed */
	// contains filtered or unexported fields
}

Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.

func (*Document_Layout) Descriptor

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

Deprecated: Use Document_Layout.ProtoReflect.Descriptor instead.

func (*Document_Layout) GetBoundingPoly

func (x *Document_Layout) GetBoundingPoly() *BoundingPoly

func (*Document_Layout) GetPageNumber

func (x *Document_Layout) GetPageNumber() int32

func (*Document_Layout) GetTextSegment

func (x *Document_Layout) GetTextSegment() *TextSegment

func (*Document_Layout) GetTextSegmentType

func (x *Document_Layout) GetTextSegmentType() Document_Layout_TextSegmentType

func (*Document_Layout) ProtoMessage

func (*Document_Layout) ProtoMessage()

func (*Document_Layout) ProtoReflect

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

func (*Document_Layout) Reset

func (x *Document_Layout) Reset()

func (*Document_Layout) String

func (x *Document_Layout) String() string

type Document_Layout_TextSegmentType

type Document_Layout_TextSegmentType int32

The type of TextSegment in the context of the original document.

const (
	// Should not be used.
	Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED Document_Layout_TextSegmentType = 0
	// The text segment is a token. e.g. word.
	Document_Layout_TOKEN Document_Layout_TextSegmentType = 1
	// The text segment is a paragraph.
	Document_Layout_PARAGRAPH Document_Layout_TextSegmentType = 2
	// The text segment is a form field.
	Document_Layout_FORM_FIELD Document_Layout_TextSegmentType = 3
	// The text segment is the name part of a form field. It will be treated
	// as child of another FORM_FIELD TextSegment if its span is subspan of
	// another TextSegment with type FORM_FIELD.
	Document_Layout_FORM_FIELD_NAME Document_Layout_TextSegmentType = 4
	// The text segment is the text content part of a form field. It will be
	// treated as child of another FORM_FIELD TextSegment if its span is
	// subspan of another TextSegment with type FORM_FIELD.
	Document_Layout_FORM_FIELD_CONTENTS Document_Layout_TextSegmentType = 5
	// The text segment is a whole table, including headers, and all rows.
	Document_Layout_TABLE Document_Layout_TextSegmentType = 6
	// The text segment is a table's headers. It will be treated as child of
	// another TABLE TextSegment if its span is subspan of another TextSegment
	// with type TABLE.
	Document_Layout_TABLE_HEADER Document_Layout_TextSegmentType = 7
	// The text segment is a row in table. It will be treated as child of
	// another TABLE TextSegment if its span is subspan of another TextSegment
	// with type TABLE.
	Document_Layout_TABLE_ROW Document_Layout_TextSegmentType = 8
	// The text segment is a cell in table. It will be treated as child of
	// another TABLE_ROW TextSegment if its span is subspan of another
	// TextSegment with type TABLE_ROW.
	Document_Layout_TABLE_CELL Document_Layout_TextSegmentType = 9
)

func (Document_Layout_TextSegmentType) Descriptor

func (Document_Layout_TextSegmentType) Enum

func (Document_Layout_TextSegmentType) EnumDescriptor

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

Deprecated: Use Document_Layout_TextSegmentType.Descriptor instead.

func (Document_Layout_TextSegmentType) Number

func (Document_Layout_TextSegmentType) String

func (Document_Layout_TextSegmentType) Type

type ExamplePayload

type ExamplePayload struct {

	// Required. The example data.
	//
	// Types that are assignable to Payload:
	//	*ExamplePayload_Image
	//	*ExamplePayload_TextSnippet
	//	*ExamplePayload_Document
	Payload isExamplePayload_Payload `protobuf_oneof:"payload"`
	// contains filtered or unexported fields
}

Example data used for training or prediction.

func (*ExamplePayload) Descriptor

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

Deprecated: Use ExamplePayload.ProtoReflect.Descriptor instead.

func (*ExamplePayload) GetDocument

func (x *ExamplePayload) GetDocument() *Document

func (*ExamplePayload) GetImage

func (x *ExamplePayload) GetImage() *Image

func (*ExamplePayload) GetPayload

func (m *ExamplePayload) GetPayload() isExamplePayload_Payload

func (*ExamplePayload) GetTextSnippet

func (x *ExamplePayload) GetTextSnippet() *TextSnippet

func (*ExamplePayload) ProtoMessage

func (*ExamplePayload) ProtoMessage()

func (*ExamplePayload) ProtoReflect

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

func (*ExamplePayload) Reset

func (x *ExamplePayload) Reset()

func (*ExamplePayload) String

func (x *ExamplePayload) String() string

type ExamplePayload_Document

type ExamplePayload_Document struct {
	// Example document.
	Document *Document `protobuf:"bytes,4,opt,name=document,proto3,oneof"`
}

type ExamplePayload_Image

type ExamplePayload_Image struct {
	// Example image.
	Image *Image `protobuf:"bytes,1,opt,name=image,proto3,oneof"`
}

type ExamplePayload_TextSnippet

type ExamplePayload_TextSnippet struct {
	// Example text.
	TextSnippet *TextSnippet `protobuf:"bytes,2,opt,name=text_snippet,json=textSnippet,proto3,oneof"`
}

type ExportDataOperationMetadata

type ExportDataOperationMetadata struct {

	// Output only. Information further describing this export data's output.
	OutputInfo *ExportDataOperationMetadata_ExportDataOutputInfo `protobuf:"bytes,1,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	// contains filtered or unexported fields
}

Details of ExportData operation.

func (*ExportDataOperationMetadata) Descriptor

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

Deprecated: Use ExportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata) GetOutputInfo

func (*ExportDataOperationMetadata) ProtoMessage

func (*ExportDataOperationMetadata) ProtoMessage()

func (*ExportDataOperationMetadata) ProtoReflect

func (*ExportDataOperationMetadata) Reset

func (x *ExportDataOperationMetadata) Reset()

func (*ExportDataOperationMetadata) String

func (x *ExportDataOperationMetadata) String() string

type ExportDataOperationMetadata_ExportDataOutputInfo

type ExportDataOperationMetadata_ExportDataOutputInfo struct {

	// The output location to which the exported data is written.
	//
	// Types that are assignable to OutputLocation:
	//	*ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory
	OutputLocation isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation `protobuf_oneof:"output_location"`
	// contains filtered or unexported fields
}

Further describes this export data's output. Supplements [OutputConfig][google.cloud.automl.v1.OutputConfig].

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Descriptor

Deprecated: Use ExportDataOperationMetadata_ExportDataOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation() isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Reset

func (*ExportDataOperationMetadata_ExportDataOutputInfo) String

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory

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

type ExportDataRequest

type ExportDataRequest struct {

	// Required. The resource name of the dataset.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location.
	OutputConfig *OutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].

func (*ExportDataRequest) Descriptor

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

Deprecated: Use ExportDataRequest.ProtoReflect.Descriptor instead.

func (*ExportDataRequest) GetName

func (x *ExportDataRequest) GetName() string

func (*ExportDataRequest) GetOutputConfig

func (x *ExportDataRequest) GetOutputConfig() *OutputConfig

func (*ExportDataRequest) ProtoMessage

func (*ExportDataRequest) ProtoMessage()

func (*ExportDataRequest) ProtoReflect

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

func (*ExportDataRequest) Reset

func (x *ExportDataRequest) Reset()

func (*ExportDataRequest) String

func (x *ExportDataRequest) String() string

type ExportModelOperationMetadata

type ExportModelOperationMetadata struct {

	// Output only. Information further describing the output of this model
	// export.
	OutputInfo *ExportModelOperationMetadata_ExportModelOutputInfo `protobuf:"bytes,2,opt,name=output_info,json=outputInfo,proto3" json:"output_info,omitempty"`
	// contains filtered or unexported fields
}

Details of ExportModel operation.

func (*ExportModelOperationMetadata) Descriptor

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

Deprecated: Use ExportModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata) GetOutputInfo

func (*ExportModelOperationMetadata) ProtoMessage

func (*ExportModelOperationMetadata) ProtoMessage()

func (*ExportModelOperationMetadata) ProtoReflect

func (*ExportModelOperationMetadata) Reset

func (x *ExportModelOperationMetadata) Reset()

func (*ExportModelOperationMetadata) String

type ExportModelOperationMetadata_ExportModelOutputInfo

type ExportModelOperationMetadata_ExportModelOutputInfo struct {

	// The full path of the Google Cloud Storage directory created, into which
	// the model will be exported.
	GcsOutputDirectory string `protobuf:"bytes,1,opt,name=gcs_output_directory,json=gcsOutputDirectory,proto3" json:"gcs_output_directory,omitempty"`
	// contains filtered or unexported fields
}

Further describes the output of model export. Supplements [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Descriptor

Deprecated: Use ExportModelOperationMetadata_ExportModelOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Reset

func (*ExportModelOperationMetadata_ExportModelOutputInfo) String

type ExportModelRequest

type ExportModelRequest struct {

	// Required. The resource name of the model to export.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired output location and configuration.
	OutputConfig *ModelExportOutputConfig `protobuf:"bytes,3,opt,name=output_config,json=outputConfig,proto3" json:"output_config,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.

func (*ExportModelRequest) Descriptor

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

Deprecated: Use ExportModelRequest.ProtoReflect.Descriptor instead.

func (*ExportModelRequest) GetName

func (x *ExportModelRequest) GetName() string

func (*ExportModelRequest) GetOutputConfig

func (x *ExportModelRequest) GetOutputConfig() *ModelExportOutputConfig

func (*ExportModelRequest) ProtoMessage

func (*ExportModelRequest) ProtoMessage()

func (*ExportModelRequest) ProtoReflect

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

func (*ExportModelRequest) Reset

func (x *ExportModelRequest) Reset()

func (*ExportModelRequest) String

func (x *ExportModelRequest) String() string

type GcsDestination

type GcsDestination struct {

	// Required. Google Cloud Storage URI to output directory, up to 2000
	// characters long.
	// Accepted forms:
	// * Prefix path: gs://bucket/directory
	// The requesting user must have write permission to the bucket.
	// The directory is created if it doesn't exist.
	OutputUriPrefix string `protobuf:"bytes,1,opt,name=output_uri_prefix,json=outputUriPrefix,proto3" json:"output_uri_prefix,omitempty"`
	// contains filtered or unexported fields
}

The Google Cloud Storage location where the output is to be written to.

func (*GcsDestination) Descriptor

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

Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead.

func (*GcsDestination) GetOutputUriPrefix

func (x *GcsDestination) GetOutputUriPrefix() string

func (*GcsDestination) ProtoMessage

func (*GcsDestination) ProtoMessage()

func (*GcsDestination) ProtoReflect

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

func (*GcsDestination) Reset

func (x *GcsDestination) Reset()

func (*GcsDestination) String

func (x *GcsDestination) String() string

type GcsSource

type GcsSource struct {

	// Required. Google Cloud Storage URIs to input files, up to 2000
	// characters long. Accepted forms:
	// * Full object path, e.g. gs://bucket/directory/object.csv
	InputUris []string `protobuf:"bytes,1,rep,name=input_uris,json=inputUris,proto3" json:"input_uris,omitempty"`
	// contains filtered or unexported fields
}

The Google Cloud Storage location for the input content.

func (*GcsSource) Descriptor

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

Deprecated: Use GcsSource.ProtoReflect.Descriptor instead.

func (*GcsSource) GetInputUris

func (x *GcsSource) GetInputUris() []string

func (*GcsSource) ProtoMessage

func (*GcsSource) ProtoMessage()

func (*GcsSource) ProtoReflect

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

func (*GcsSource) Reset

func (x *GcsSource) Reset()

func (*GcsSource) String

func (x *GcsSource) String() string

type GetAnnotationSpecRequest

type GetAnnotationSpecRequest struct {

	// Required. The resource name of the annotation spec to retrieve.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].

func (*GetAnnotationSpecRequest) Descriptor

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

Deprecated: Use GetAnnotationSpecRequest.ProtoReflect.Descriptor instead.

func (*GetAnnotationSpecRequest) GetName

func (x *GetAnnotationSpecRequest) GetName() string

func (*GetAnnotationSpecRequest) ProtoMessage

func (*GetAnnotationSpecRequest) ProtoMessage()

func (*GetAnnotationSpecRequest) ProtoReflect

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

func (*GetAnnotationSpecRequest) Reset

func (x *GetAnnotationSpecRequest) Reset()

func (*GetAnnotationSpecRequest) String

func (x *GetAnnotationSpecRequest) String() string

type GetDatasetRequest

type GetDatasetRequest struct {

	// Required. The resource name of the dataset to retrieve.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].

func (*GetDatasetRequest) Descriptor

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

Deprecated: Use GetDatasetRequest.ProtoReflect.Descriptor instead.

func (*GetDatasetRequest) GetName

func (x *GetDatasetRequest) GetName() string

func (*GetDatasetRequest) ProtoMessage

func (*GetDatasetRequest) ProtoMessage()

func (*GetDatasetRequest) ProtoReflect

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

func (*GetDatasetRequest) Reset

func (x *GetDatasetRequest) Reset()

func (*GetDatasetRequest) String

func (x *GetDatasetRequest) String() string

type GetModelEvaluationRequest

type GetModelEvaluationRequest struct {

	// Required. Resource name for the model evaluation.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].

func (*GetModelEvaluationRequest) Descriptor

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

Deprecated: Use GetModelEvaluationRequest.ProtoReflect.Descriptor instead.

func (*GetModelEvaluationRequest) GetName

func (x *GetModelEvaluationRequest) GetName() string

func (*GetModelEvaluationRequest) ProtoMessage

func (*GetModelEvaluationRequest) ProtoMessage()

func (*GetModelEvaluationRequest) ProtoReflect

func (*GetModelEvaluationRequest) Reset

func (x *GetModelEvaluationRequest) Reset()

func (*GetModelEvaluationRequest) String

func (x *GetModelEvaluationRequest) String() string

type GetModelRequest

type GetModelRequest struct {

	// Required. Resource name of the model.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].

func (*GetModelRequest) Descriptor

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

Deprecated: Use GetModelRequest.ProtoReflect.Descriptor instead.

func (*GetModelRequest) GetName

func (x *GetModelRequest) GetName() string

func (*GetModelRequest) ProtoMessage

func (*GetModelRequest) ProtoMessage()

func (*GetModelRequest) ProtoReflect

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

func (*GetModelRequest) Reset

func (x *GetModelRequest) Reset()

func (*GetModelRequest) String

func (x *GetModelRequest) String() string

type Image

type Image struct {

	// Input only. The data representing the image.
	// For Predict calls [image_bytes][google.cloud.automl.v1.Image.image_bytes] must be set .
	//
	// Types that are assignable to Data:
	//	*Image_ImageBytes
	Data isImage_Data `protobuf_oneof:"data"`
	// Output only. HTTP URI to the thumbnail image.
	ThumbnailUri string `protobuf:"bytes,4,opt,name=thumbnail_uri,json=thumbnailUri,proto3" json:"thumbnail_uri,omitempty"`
	// contains filtered or unexported fields
}

A representation of an image. Only images up to 30MB in size are supported.

func (*Image) Descriptor

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

Deprecated: Use Image.ProtoReflect.Descriptor instead.

func (*Image) GetData

func (m *Image) GetData() isImage_Data

func (*Image) GetImageBytes

func (x *Image) GetImageBytes() []byte

func (*Image) GetThumbnailUri

func (x *Image) GetThumbnailUri() string

func (*Image) ProtoMessage

func (*Image) ProtoMessage()

func (*Image) ProtoReflect

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

func (*Image) Reset

func (x *Image) Reset()

func (*Image) String

func (x *Image) String() string

type ImageClassificationDatasetMetadata

type ImageClassificationDatasetMetadata struct {

	// Required. Type of the classification problem.
	ClassificationType ClassificationType `` /* 163-byte string literal not displayed */
	// contains filtered or unexported fields
}

Dataset metadata that is specific to image classification.

func (*ImageClassificationDatasetMetadata) Descriptor

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

Deprecated: Use ImageClassificationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationDatasetMetadata) GetClassificationType

func (x *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*ImageClassificationDatasetMetadata) ProtoMessage

func (*ImageClassificationDatasetMetadata) ProtoMessage()

func (*ImageClassificationDatasetMetadata) ProtoReflect

func (*ImageClassificationDatasetMetadata) Reset

func (*ImageClassificationDatasetMetadata) String

type ImageClassificationModelDeploymentMetadata

type ImageClassificationModelDeploymentMetadata struct {

	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	//
	// [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// contains filtered or unexported fields
}

Model deployment metadata specific to Image Classification.

func (*ImageClassificationModelDeploymentMetadata) Descriptor

Deprecated: Use ImageClassificationModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelDeploymentMetadata) GetNodeCount

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage

func (*ImageClassificationModelDeploymentMetadata) ProtoReflect

func (*ImageClassificationModelDeploymentMetadata) Reset

func (*ImageClassificationModelDeploymentMetadata) String

type ImageClassificationModelMetadata

type ImageClassificationModelMetadata struct {

	// Optional. The ID of the `base` model. If it is specified, the new model
	// will be created based on the `base` model. Otherwise, the new model will be
	// created from scratch. The `base` model must be in the same
	// `project` and `location` as the new model to create, and have the same
	// `model_type`.
	BaseModelId string `protobuf:"bytes,1,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
	// The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// `train_cost` will be equal or less than this value. If further model
	// training ceases to provide any improvements, it will stop without using
	// full budget and the stop_reason will be `MODEL_CONVERGED`.
	// Note, node_hour  = actual_hour * number_of_nodes_invovled.
	// For model type `cloud`(default), the train budget must be between 8,000
	// and 800,000 milli node hours, inclusive. The default value is 192, 000
	// which represents one day in wall time. For model type
	// `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`,
	// `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`,
	// `mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000
	// and 100,000 milli node hours, inclusive. The default value is 24, 000 which
	// represents one day in wall time.
	TrainBudgetMilliNodeHours int64 `` /* 144-byte string literal not displayed */
	// Output only. The actual train cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed the train budget.
	TrainCostMilliNodeHours int64 `` /* 138-byte string literal not displayed */
	// Output only. The reason that this create model operation stopped,
	// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
	// Optional. Type of the model. The available values are:
	// *   `cloud` - Model to be used via prediction calls to AutoML API.
	//               This is the default value.
	// *   `mobile-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards. Expected to have low latency, but
	//               may have lower prediction quality than other models.
	// *   `mobile-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.
	// *   `mobile-high-accuracy-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.  Expected to have a higher
	//               latency, but should also have a higher prediction quality
	//               than other models.
	// *   `mobile-core-ml-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
	//               ML afterwards. Expected to have low latency, but may have
	//               lower prediction quality than other models.
	// *   `mobile-core-ml-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
	//               ML afterwards.
	// *   `mobile-core-ml-high-accuracy-1` - A model that, in addition to
	//               providing prediction via AutoML API, can also be exported
	//               (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
	//               Core ML afterwards.  Expected to have a higher latency, but
	//               should also have a higher prediction quality than other
	//               models.
	ModelType string `protobuf:"bytes,7,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
	// Output only. An approximate number of online prediction QPS that can
	// be supported by this model per each node on which it is deployed.
	NodeQps float64 `protobuf:"fixed64,13,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
	// Output only. The number of nodes this model is deployed on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the node_qps field.
	NodeCount int64 `protobuf:"varint,14,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// contains filtered or unexported fields
}

Model metadata for image classification.

func (*ImageClassificationModelMetadata) Descriptor

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

Deprecated: Use ImageClassificationModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelMetadata) GetBaseModelId

func (x *ImageClassificationModelMetadata) GetBaseModelId() string

func (*ImageClassificationModelMetadata) GetModelType

func (x *ImageClassificationModelMetadata) GetModelType() string

func (*ImageClassificationModelMetadata) GetNodeCount

func (x *ImageClassificationModelMetadata) GetNodeCount() int64

func (*ImageClassificationModelMetadata) GetNodeQps

func (x *ImageClassificationModelMetadata) GetNodeQps() float64

func (*ImageClassificationModelMetadata) GetStopReason

func (x *ImageClassificationModelMetadata) GetStopReason() string

func (*ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours

func (x *ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageClassificationModelMetadata) GetTrainCostMilliNodeHours

func (x *ImageClassificationModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageClassificationModelMetadata) ProtoMessage

func (*ImageClassificationModelMetadata) ProtoMessage()

func (*ImageClassificationModelMetadata) ProtoReflect

func (*ImageClassificationModelMetadata) Reset

func (*ImageClassificationModelMetadata) String

type ImageObjectDetectionAnnotation

type ImageObjectDetectionAnnotation struct {

	// Output only. The rectangle representing the object location.
	BoundingBox *BoundingPoly `protobuf:"bytes,1,opt,name=bounding_box,json=boundingBox,proto3" json:"bounding_box,omitempty"`
	// Output only. The confidence that this annotation is positive for the parent example,
	// value in [0, 1], higher means higher positivity confidence.
	Score float32 `protobuf:"fixed32,2,opt,name=score,proto3" json:"score,omitempty"`
	// contains filtered or unexported fields
}

Annotation details for image object detection.

func (*ImageObjectDetectionAnnotation) Descriptor

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

Deprecated: Use ImageObjectDetectionAnnotation.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionAnnotation) GetBoundingBox

func (x *ImageObjectDetectionAnnotation) GetBoundingBox() *BoundingPoly

func (*ImageObjectDetectionAnnotation) GetScore

func (*ImageObjectDetectionAnnotation) ProtoMessage

func (*ImageObjectDetectionAnnotation) ProtoMessage()

func (*ImageObjectDetectionAnnotation) ProtoReflect

func (*ImageObjectDetectionAnnotation) Reset

func (x *ImageObjectDetectionAnnotation) Reset()

func (*ImageObjectDetectionAnnotation) String

type ImageObjectDetectionDatasetMetadata

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

Dataset metadata specific to image object detection.

func (*ImageObjectDetectionDatasetMetadata) Descriptor

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

Deprecated: Use ImageObjectDetectionDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()

func (*ImageObjectDetectionDatasetMetadata) ProtoReflect

func (*ImageObjectDetectionDatasetMetadata) Reset

func (*ImageObjectDetectionDatasetMetadata) String

type ImageObjectDetectionEvaluationMetrics

type ImageObjectDetectionEvaluationMetrics struct {

	// Output only. The total number of bounding boxes (i.e. summed over all
	// images) the ground truth used to create this evaluation had.
	EvaluatedBoundingBoxCount int32 `` /* 141-byte string literal not displayed */
	// Output only. The bounding boxes match metrics for each
	// Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
	// and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
	// pair.
	BoundingBoxMetricsEntries []*BoundingBoxMetricsEntry `` /* 140-byte string literal not displayed */
	// Output only. The single metric for bounding boxes evaluation:
	// the mean_average_precision averaged over all bounding_box_metrics_entries.
	BoundingBoxMeanAveragePrecision float32 `` /* 162-byte string literal not displayed */
	// contains filtered or unexported fields
}

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

func (*ImageObjectDetectionEvaluationMetrics) Descriptor

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

Deprecated: Use ImageObjectDetectionEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision

func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries

func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries() []*BoundingBoxMetricsEntry

func (*ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount

func (x *ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage()

func (*ImageObjectDetectionEvaluationMetrics) ProtoReflect

func (*ImageObjectDetectionEvaluationMetrics) Reset

func (*ImageObjectDetectionEvaluationMetrics) String

type ImageObjectDetectionModelDeploymentMetadata

type ImageObjectDetectionModelDeploymentMetadata struct {

	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	//
	// [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// contains filtered or unexported fields
}

Model deployment metadata specific to Image Object Detection.

func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor

Deprecated: Use ImageObjectDetectionModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelDeploymentMetadata) GetNodeCount

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoReflect

func (*ImageObjectDetectionModelDeploymentMetadata) Reset

func (*ImageObjectDetectionModelDeploymentMetadata) String

type ImageObjectDetectionModelMetadata

type ImageObjectDetectionModelMetadata struct {

	// Optional. Type of the model. The available values are:
	// *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
	//               calls to AutoML API. Expected to have a higher latency, but
	//               should also have a higher prediction quality than other
	//               models.
	// *   `cloud-low-latency-1` -  A model to be used via prediction
	//               calls to AutoML API. Expected to have low latency, but may
	//               have lower prediction quality than other models.
	// *   `mobile-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards. Expected to have low latency, but
	//               may have lower prediction quality than other models.
	// *   `mobile-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.
	// *   `mobile-high-accuracy-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
	//               with TensorFlow afterwards.  Expected to have a higher
	//               latency, but should also have a higher prediction quality
	//               than other models.
	ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
	// Output only. The number of nodes this model is deployed on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the qps_per_node field.
	NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// Output only. An approximate number of online prediction QPS that can
	// be supported by this model per each node on which it is deployed.
	NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
	// Output only. The reason that this create model operation stopped,
	// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
	// The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// `train_cost` will be equal or less than this value. If further model
	// training ceases to provide any improvements, it will stop without using
	// full budget and the stop_reason will be `MODEL_CONVERGED`.
	// Note, node_hour  = actual_hour * number_of_nodes_invovled.
	// For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
	// the train budget must be between 20,000 and 900,000 milli node hours,
	// inclusive. The default value is 216, 000 which represents one day in
	// wall time.
	// For model type `mobile-low-latency-1`, `mobile-versatile-1`,
	// `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
	// `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
	// budget must be between 1,000 and 100,000 milli node hours, inclusive.
	// The default value is 24, 000 which represents one day in wall time.
	TrainBudgetMilliNodeHours int64 `` /* 143-byte string literal not displayed */
	// Output only. The actual train cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed the train budget.
	TrainCostMilliNodeHours int64 `` /* 137-byte string literal not displayed */
	// contains filtered or unexported fields
}

Model metadata specific to image object detection.

func (*ImageObjectDetectionModelMetadata) Descriptor

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

Deprecated: Use ImageObjectDetectionModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelMetadata) GetModelType

func (x *ImageObjectDetectionModelMetadata) GetModelType() string

func (*ImageObjectDetectionModelMetadata) GetNodeCount

func (x *ImageObjectDetectionModelMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelMetadata) GetNodeQps

func (*ImageObjectDetectionModelMetadata) GetStopReason

func (x *ImageObjectDetectionModelMetadata) GetStopReason() string

func (*ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours

func (x *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours

func (x *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) ProtoMessage

func (*ImageObjectDetectionModelMetadata) ProtoMessage()

func (*ImageObjectDetectionModelMetadata) ProtoReflect

func (*ImageObjectDetectionModelMetadata) Reset

func (*ImageObjectDetectionModelMetadata) String

type Image_ImageBytes

type Image_ImageBytes struct {
	// Image content represented as a stream of bytes.
	// Note: As with all `bytes` fields, protobuffers use a pure binary
	// representation, whereas JSON representations use base64.
	ImageBytes []byte `protobuf:"bytes,1,opt,name=image_bytes,json=imageBytes,proto3,oneof"`
}

type ImportDataOperationMetadata

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

Details of ImportData operation.

func (*ImportDataOperationMetadata) Descriptor

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

Deprecated: Use ImportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ImportDataOperationMetadata) ProtoMessage

func (*ImportDataOperationMetadata) ProtoMessage()

func (*ImportDataOperationMetadata) ProtoReflect

func (*ImportDataOperationMetadata) Reset

func (x *ImportDataOperationMetadata) Reset()

func (*ImportDataOperationMetadata) String

func (x *ImportDataOperationMetadata) String() string

type ImportDataRequest

type ImportDataRequest struct {

	// Required. Dataset name. Dataset must already exist. All imported
	// annotations and examples will be added.
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// Required. The desired input location and its domain specific semantics,
	// if any.
	InputConfig *InputConfig `protobuf:"bytes,3,opt,name=input_config,json=inputConfig,proto3" json:"input_config,omitempty"`
	// contains filtered or unexported fields
}

Request message for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].

func (*ImportDataRequest) Descriptor

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

Deprecated: Use ImportDataRequest.ProtoReflect.Descriptor instead.

func (*ImportDataRequest) GetInputConfig

func (x *ImportDataRequest) GetInputConfig() *InputConfig

func (*ImportDataRequest) GetName

func (x *ImportDataRequest) GetName() string

func (*ImportDataRequest) ProtoMessage

func (*ImportDataRequest) ProtoMessage()

func (*ImportDataRequest) ProtoReflect

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

func (*ImportDataRequest) Reset

func (x *ImportDataRequest) Reset()

func (*ImportDataRequest) String

func (x *ImportDataRequest) String() string

type InputConfig

type InputConfig struct {

	// The source of the input.
	//
	// Types that are assignable to Source:
	//	*InputConfig_GcsSource
	Source isInputConfig_Source `protobuf_oneof:"source"`
	// Additional domain-specific parameters describing the semantic of the
	// imported data, any string must be up to 25000
	// characters long.
	//
	// <h4>AutoML Tables</h4>
	//
	// `schema_inference_version`
	// : (integer) This value must be supplied.
	//   The version of the
	//   algorithm to use for the initial inference of the
	//   column data types of the imported table. Allowed values: "1".
	Params map[string]string `` /* 153-byte string literal not displayed */
	// contains filtered or unexported fields
}

Input configuration for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action.

The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an "example" file (that is, image, video etc.) with identical content (even if it had different `GCS_FILE_PATH`) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same `ML_USE` and `GCS_FILE_PATH`, if it is not, then these values are nondeterministically selected from the given ones.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

<h4>AutoML Vision</h4>

<div class="ds-selector-tabs"><section><h5>Classification</h5>

See [Preparing your training data](https://cloud.google.com/vision/automl/docs/prepare) for more information.

CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH,LABEL,LABEL,...

* `ML_USE` - Identifies the data set that the current row (file) applies to.

This value can be one of the following:
* `TRAIN` - Rows in this file are used to train the model.
* `TEST` - Rows in this file are used to test the model during training.
* `UNASSIGNED` - Rows in this file are not categorized. They are
   Automatically divided into train and test data. 80% for training and
   20% for testing.

* `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to

30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP,
.TIFF, .ICO.

* `LABEL` - A label that identifies the object in the image.

For the `MULTICLASS` classification type, at most one `LABEL` is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no `LABEL`.

Some sample rows:

TRAIN,gs://folder/image1.jpg,daisy
TEST,gs://folder/image2.jpg,dandelion,tulip,rose
UNASSIGNED,gs://folder/image3.jpg,daisy
UNASSIGNED,gs://folder/image4.jpg

</section><section><h5>Object Detection</h5> See [Preparing your training data](https://cloud.google.com/vision/automl/object-detection/docs/prepare) for more information.

A CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)

* `ML_USE` - Identifies the data set that the current row (file) applies to.

This value can be one of the following:
* `TRAIN` - Rows in this file are used to train the model.
* `TEST` - Rows in this file are used to test the model during training.
* `UNASSIGNED` - Rows in this file are not categorized. They are
   Automatically divided into train and test data. 80% for training and
   20% for testing.

* `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to

30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image
is assumed to be exhaustively labeled.

* `LABEL` - A label that identifies the object in the image specified by the

`BOUNDING_BOX`.

* `BOUNDING BOX` - The vertices of an object in the example image.

 The minimum allowed `BOUNDING_BOX` edge length is 0.01, and no more than
 500 `BOUNDING_BOX` instances per image are allowed (one `BOUNDING_BOX`
 per line). If an image has no looked for objects then it should be
 mentioned just once with no LABEL and the ",,,,,,," in place of the
`BOUNDING_BOX`.

**Four sample rows:**

  TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
  TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
  UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
  TEST,gs://folder/im3.png,,,,,,,,,
</section>

</div>

<h4>AutoML Video Intelligence</h4>

<div class="ds-selector-tabs"><section><h5>Classification</h5>

See [Preparing your training data](https://cloud.google.com/video-intelligence/automl/docs/prepare) for more information.

CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH

For `ML_USE`, do not use `VALIDATE`.

`GCS_FILE_PATH` is the path to another .csv file that describes training example for a given `ML_USE`, using the following row format:

GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)

Here `GCS_FILE_PATH` leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

`TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place of `LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END`.

Sample top level CSV file:

TRAIN,gs://folder/train_videos.csv
TEST,gs://folder/test_videos.csv
UNASSIGNED,gs://folder/other_videos.csv

Sample rows of a CSV file for a particular ML_USE:

gs://folder/video1.avi,car,120,180.000021
gs://folder/video1.avi,bike,150,180.000021
gs://folder/vid2.avi,car,0,60.5
gs://folder/vid3.avi,,,

</section><section><h5>Object Tracking</h5>

See [Preparing your training data](/video-intelligence/automl/object-tracking/docs/prepare) for more information.

CSV file(s) with each line in format:

ML_USE,GCS_FILE_PATH

For `ML_USE`, do not use `VALIDATE`.

`GCS_FILE_PATH` is the path to another .csv file that describes training example for a given `ML_USE`, using the following row format:

GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX

or

GCS_FILE_PATH,,,,,,,,,,

Here `GCS_FILE_PATH` leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing `INSTANCE_ID`s can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same `INSTANCE_ID` is given to it.

`TIMESTAMP` must be within the length of the video, the `BOUNDING_BOX` is assumed to be drawn on the closest video's frame to the `TIMESTAMP`. Any mentioned by the `TIMESTAMP` frame is expected to be exhaustively labeled and no more than 500 `BOUNDING_BOX`-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of `LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX`.

Sample top level CSV file:

TRAIN,gs://folder/train_videos.csv
TEST,gs://folder/test_videos.csv
UNASSIGNED,gs://folder/other_videos.csv

Seven sample rows of a CSV file for a particular ML_USE:

   gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
   gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
   gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
   gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
   gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
   gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
   gs://folder/video2.avi,,,,,,,,,,,
</section>

</div>

<h4>AutoML Natural Language</h4>

<div class="ds-selector-tabs"><section><h5>Entity Extraction</h5>

See [Preparing your training data](/natural-language/automl/entity-analysis/docs/prepare) for more information.

One or more CSV file(s) with each line in the following format:

ML_USE,GCS_FILE_PATH

* `ML_USE` - Identifies the data set that the current row (file) applies to.

This value can be one of the following:
* `TRAIN` - Rows in this file are used to train the model.
* `TEST` - Rows in this file are used to test the model during training.
* `UNASSIGNED` - Rows in this file are not categorized. They are
   Automatically divided into train and test data. 80% for training and
   20% for testing..

* `GCS_FILE_PATH` - a Identifies JSON Lines (.JSONL) file stored in

Google Cloud Storage that contains in-line text in-line as documents
for model training.

After the training data set has been determined from the `TRAIN` and `UNASSIGNED` CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation.

For example:

TRAIN,gs://folder/file1.jsonl
VALIDATE,gs://folder/file2.jsonl
TEST,gs://folder/file3.jsonl

**In-line JSONL files**

In-line .JSONL files contain, per line, a JSON document that wraps a [`text_snippet`][google.cloud.automl.v1.TextSnippet] field followed by one or more [`annotations`][google.cloud.automl.v1.AnnotationPayload] fields, which have `display_name` and `text_extraction` fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n).

The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal".

Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded.

For example:

{
  "text_snippet": {
    "content": "dog car cat"
  },
  "annotations": [
     {
       "display_name": "animal",
       "text_extraction": {
         "text_segment": {"start_offset": 0, "end_offset": 2}
      }
     },
     {
      "display_name": "vehicle",
       "text_extraction": {
         "text_segment": {"start_offset": 4, "end_offset": 6}
       }
     },
     {
       "display_name": "animal",
       "text_extraction": {
         "text_segment": {"start_offset": 8, "end_offset": 10}
       }
     }
 ]
}\n
{
   "text_snippet": {
     "content": "This dog is good."
   },
   "annotations": [
      {
        "display_name": "animal",
        "text_extraction": {
          "text_segment": {"start_offset": 5, "end_offset": 7}
        }
      }
   ]
}

**JSONL files that reference documents**

.JSONL files contain, per line, a JSON document that wraps a `input_config` that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n).

Supported document extensions: .PDF, .TIF, .TIFF

For example:

{
  "document": {
    "input_config": {
      "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
      }
    }
  }
}\n
{
  "document": {
    "input_config": {
      "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
      }
    }
  }
}

**In-line JSONL files with document layout information**

**Note:** You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or `exportData`.

In-line .JSONL files for documents contain, per line, a JSON document that wraps a `document` field that provides the textual content of the document and the layout information.

For example:

{
  "document": {
          "document_text": {
            "content": "dog car cat"
          }
          "layout": [
            {
              "text_segment": {
                "start_offset": 0,
                "end_offset": 11,
               },
               "page_number": 1,
               "bounding_poly": {
                  "normalized_vertices": [
                    {"x": 0.1, "y": 0.1},
                    {"x": 0.1, "y": 0.3},
                    {"x": 0.3, "y": 0.3},
                    {"x": 0.3, "y": 0.1},
                  ],
                },
                "text_segment_type": TOKEN,
            }
          ],
          "document_dimensions": {
            "width": 8.27,
            "height": 11.69,
            "unit": INCH,
          }
          "page_count": 3,
        },
        "annotations": [
          {
            "display_name": "animal",
            "text_extraction": {
              "text_segment": {"start_offset": 0, "end_offset": 3}
            }
          },
          {
            "display_name": "vehicle",
            "text_extraction": {
              "text_segment": {"start_offset": 4, "end_offset": 7}
            }
          },
          {
            "display_name": "animal",
            "text_extraction": {
              "text_segment": {"start_offset": 8, "end_offset": 11}
            }
          },
        ],

</section><section><h5>Classification</h5>

See [Preparing your training data](https://cloud.google.com/natural-language/automl/docs/prepare) for more information.

One or more CSV file(s) with each line in the following format:

ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...

* `ML_USE` - Identifies the data set that the current row (file) applies to.

This value can be one of the following:
* `TRAIN` - Rows in this file are used to train the model.
* `TEST` - Rows in this file are used to test the model during training.
* `UNASSIGNED` - Rows in this file are not categorized. They are
   Automatically divided into train and test data. 80% for training and
   20% for testing.

* `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If

the column content is a valid Google Cloud Storage file path, that is,
prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if
the content is enclosed in double quotes (""), it is treated as a
`TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a
file with supported extension and UTF-8 encoding, for example,
"gs://folder/content.txt" AutoML imports the file content
as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content
excluding quotes. In both cases, size of the content must be 10MB or
less in size. For zip files, the size of each file inside the zip must be
10MB or less in size.

For the `MULTICLASS` classification type, at most one `LABEL` is allowed.

The `ML_USE` and `LABEL` columns are optional.
Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

A maximum of 100 unique labels are allowed per CSV row.

Sample rows:

TRAIN,"They have bad food and very rude",RudeService,BadFood
gs://folder/content.txt,SlowService
TEST,gs://folder/document.pdf
VALIDATE,gs://folder/text_files.zip,BadFood

</section><section><h5>Sentiment Analysis</h5>

See [Preparing your training data](https://cloud.google.com/natural-language/automl/docs/prepare) for more information.

CSV file(s) with each line in format:

ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT

* `ML_USE` - Identifies the data set that the current row (file) applies to.

This value can be one of the following:
* `TRAIN` - Rows in this file are used to train the model.
* `TEST` - Rows in this file are used to test the model during training.
* `UNASSIGNED` - Rows in this file are not categorized. They are
   Automatically divided into train and test data. 80% for training and
   20% for testing.

* `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If

the column content is a valid  Google Cloud Storage file path, that is,
prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if
the content is enclosed in double quotes (""), it is treated as a
`TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a
file with supported extension and UTF-8 encoding, for example,
"gs://folder/content.txt" AutoML imports the file content
as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content
excluding quotes. In both cases, size of the content must be 128kB or
less in size. For zip files, the size of each file inside the zip must be
128kB or less in size.

The `ML_USE` and `SENTIMENT` columns are optional.
Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

* `SENTIMENT` - An integer between 0 and

Dataset.text_sentiment_dataset_metadata.sentiment_max
(inclusive). Describes the ordinal of the sentiment - higher
value means a more positive sentiment. All the values are
completely relative, i.e. neither 0 needs to mean a negative or
neutral sentiment nor sentiment_max needs to mean a positive one -
it is just required that 0 is the least positive sentiment
in the data, and sentiment_max is the  most positive one.
The SENTIMENT shouldn't be confused with "score" or "magnitude"
from the previous Natural Language Sentiment Analysis API.
All SENTIMENT values between 0 and sentiment_max must be
represented in the imported data. On prediction the same 0 to
sentiment_max range will be used. The difference between
neighboring sentiment values needs not to be uniform, e.g. 1 and
2 may be similar whereas the difference between 2 and 3 may be
large.

Sample rows:

  TRAIN,"@freewrytin this is way too good for your product",2
  gs://folder/content.txt,3
  TEST,gs://folder/document.pdf
  VALIDATE,gs://folder/text_files.zip,2
</section>

</div>

<h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected">

See [Preparing your training data](https://cloud.google.com/automl-tables/docs/prepare) for more information.

You can use either [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All input is concatenated into a single

[primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id]

**For gcs_source:**

CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller.

First three sample rows of a CSV file: <pre> "Id","First Name","Last Name","Dob","Addresses"

"1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"

"2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> **For bigquery_source:**

An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel.

</section>

</div>

**Input field definitions:**

`ML_USE` : ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED")

Describes how the given example (file) should be used for model
training. "UNASSIGNED" can be used when user has no preference.

`GCS_FILE_PATH` : The path to a file on Google Cloud Storage. For example,

"gs://folder/image1.png".

`LABEL` : A display name of an object on an image, video etc., e.g. "dog".

Must be up to 32 characters long and can consist only of ASCII
Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
For each label an AnnotationSpec is created which display_name
becomes the label; AnnotationSpecs are given back in predictions.

`INSTANCE_ID` : A positive integer that identifies a specific instance of a

labeled entity on an example. Used e.g. to track two cars on
a video while being able to tell apart which one is which.

`BOUNDING_BOX` : (`VERTEX,VERTEX,VERTEX,VERTEX` | `VERTEX,,,VERTEX,,`)

A rectangle parallel to the frame of the example (image,
video). If 4 vertices are given they are connected by edges
in the order provided, if 2 are given they are recognized
as diagonally opposite vertices of the rectangle.

`VERTEX` : (`COORDINATE,COORDINATE`)

First coordinate is horizontal (x), the second is vertical (y).

`COORDINATE` : A float in 0 to 1 range, relative to total length of

image or video in given dimension. For fractions the
leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
Point 0,0 is in top left.

`TIME_SEGMENT_START` : (`TIME_OFFSET`)

Expresses a beginning, inclusive, of a time segment
within an example that has a time dimension
(e.g. video).

`TIME_SEGMENT_END` : (`TIME_OFFSET`)

Expresses an end, exclusive, of a time segment within
n example that has a time dimension (e.g. video).

`TIME_OFFSET` : A number of seconds as measured from the start of an

example (e.g. video). Fractions are allowed, up to a
microsecond precision. "inf" is allowed, and it means the end
of the example.

`TEXT_SNIPPET` : The content of a text snippet, UTF-8 encoded, enclosed within

double quotes ("").

`DOCUMENT` : A field that provides the textual content with document and the layout

 information.

**Errors:**

If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
nothing is imported. Regardless of overall success or failure the per-row
failures, up to a certain count cap, is listed in
Operation.metadata.partial_failures.

func (*InputConfig) Descriptor

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

Deprecated: Use InputConfig.ProtoReflect.Descriptor instead.

func (*InputConfig) GetGcsSource

func (x *InputConfig) GetGcsSource() *GcsSource

func (*InputConfig) GetParams

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

func (*InputConfig) GetSource

func (m *InputConfig) GetSource() isInputConfig_Source

func (*InputConfig)