genproto: google.golang.org/genproto/googleapis/cloud/automl/v1 Index | Files

package automl

import "google.golang.org/genproto/googleapis/cloud/automl/v1"

Index

Package Files

annotation_payload.pb.go annotation_spec.pb.go classification.pb.go data_items.pb.go dataset.pb.go detection.pb.go geometry.pb.go image.pb.go io.pb.go model.pb.go model_evaluation.pb.go operations.pb.go prediction_service.pb.go service.pb.go text.pb.go text_extraction.pb.go text_segment.pb.go text_sentiment.pb.go translation.pb.go

Variables

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.

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.

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.

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.

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

func RegisterAutoMlServer Uses

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

func RegisterPredictionServiceServer Uses

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

type AnnotationPayload Uses

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 Uses

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

Deprecated: Use AnnotationPayload.ProtoReflect.Descriptor instead.

func (*AnnotationPayload) GetAnnotationSpecId Uses

func (x *AnnotationPayload) GetAnnotationSpecId() string

func (*AnnotationPayload) GetClassification Uses

func (x *AnnotationPayload) GetClassification() *ClassificationAnnotation

func (*AnnotationPayload) GetDetail Uses

func (m *AnnotationPayload) GetDetail() isAnnotationPayload_Detail

func (*AnnotationPayload) GetDisplayName Uses

func (x *AnnotationPayload) GetDisplayName() string

func (*AnnotationPayload) GetImageObjectDetection Uses

func (x *AnnotationPayload) GetImageObjectDetection() *ImageObjectDetectionAnnotation

func (*AnnotationPayload) GetTextExtraction Uses

func (x *AnnotationPayload) GetTextExtraction() *TextExtractionAnnotation

func (*AnnotationPayload) GetTextSentiment Uses

func (x *AnnotationPayload) GetTextSentiment() *TextSentimentAnnotation

func (*AnnotationPayload) GetTranslation Uses

func (x *AnnotationPayload) GetTranslation() *TranslationAnnotation

func (*AnnotationPayload) ProtoMessage Uses

func (*AnnotationPayload) ProtoMessage()

func (*AnnotationPayload) ProtoReflect Uses

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

func (*AnnotationPayload) Reset Uses

func (x *AnnotationPayload) Reset()

func (*AnnotationPayload) String Uses

func (x *AnnotationPayload) String() string

type AnnotationPayload_Classification Uses

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

type AnnotationPayload_ImageObjectDetection Uses

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 Uses

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 Uses

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 Uses

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

type AnnotationSpec Uses

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 Uses

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

Deprecated: Use AnnotationSpec.ProtoReflect.Descriptor instead.

func (*AnnotationSpec) GetDisplayName Uses

func (x *AnnotationSpec) GetDisplayName() string

func (*AnnotationSpec) GetExampleCount Uses

func (x *AnnotationSpec) GetExampleCount() int32

func (*AnnotationSpec) GetName Uses

func (x *AnnotationSpec) GetName() string

func (*AnnotationSpec) ProtoMessage Uses

func (*AnnotationSpec) ProtoMessage()

func (*AnnotationSpec) ProtoReflect Uses

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

func (*AnnotationSpec) Reset Uses

func (x *AnnotationSpec) Reset()

func (*AnnotationSpec) String Uses

func (x *AnnotationSpec) String() string

type AutoMlClient Uses

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 Uses

func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient

type AutoMlServer Uses

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 Uses

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 Uses

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

Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictInputConfig) GetGcsSource Uses

func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource

func (*BatchPredictInputConfig) GetSource Uses

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source

func (*BatchPredictInputConfig) ProtoMessage Uses

func (*BatchPredictInputConfig) ProtoMessage()

func (*BatchPredictInputConfig) ProtoReflect Uses

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

func (*BatchPredictInputConfig) Reset Uses

func (x *BatchPredictInputConfig) Reset()

func (*BatchPredictInputConfig) String Uses

func (x *BatchPredictInputConfig) String() string

type BatchPredictInputConfig_GcsSource Uses

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 Uses

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 Uses

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

Deprecated: Use BatchPredictOperationMetadata.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata) GetInputConfig Uses

func (x *BatchPredictOperationMetadata) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictOperationMetadata) GetOutputInfo Uses

func (x *BatchPredictOperationMetadata) GetOutputInfo() *BatchPredictOperationMetadata_BatchPredictOutputInfo

func (*BatchPredictOperationMetadata) ProtoMessage Uses

func (*BatchPredictOperationMetadata) ProtoMessage()

func (*BatchPredictOperationMetadata) ProtoReflect Uses

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

func (*BatchPredictOperationMetadata) Reset Uses

func (x *BatchPredictOperationMetadata) Reset()

func (*BatchPredictOperationMetadata) String Uses

func (x *BatchPredictOperationMetadata) String() string

type BatchPredictOperationMetadata_BatchPredictOutputInfo Uses

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 Uses

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

Deprecated: Use BatchPredictOperationMetadata_BatchPredictOutputInfo.ProtoReflect.Descriptor instead.

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetGcsOutputDirectory() string

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation Uses

func (m *BatchPredictOperationMetadata_BatchPredictOutputInfo) GetOutputLocation() isBatchPredictOperationMetadata_BatchPredictOutputInfo_OutputLocation

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage Uses

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoMessage()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) ProtoReflect Uses

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

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) Reset()

func (*BatchPredictOperationMetadata_BatchPredictOutputInfo) String Uses

func (x *BatchPredictOperationMetadata_BatchPredictOutputInfo) String() string

type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory Uses

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 Uses

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 Uses

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

Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead.

func (*BatchPredictOutputConfig) GetDestination Uses

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination

func (*BatchPredictOutputConfig) GetGcsDestination Uses

func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination

func (*BatchPredictOutputConfig) ProtoMessage Uses

func (*BatchPredictOutputConfig) ProtoMessage()

func (*BatchPredictOutputConfig) ProtoReflect Uses

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

func (*BatchPredictOutputConfig) Reset Uses

func (x *BatchPredictOutputConfig) Reset()

func (*BatchPredictOutputConfig) String Uses

func (x *BatchPredictOutputConfig) String() string

type BatchPredictOutputConfig_GcsDestination Uses

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 Uses

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 `protobuf:"bytes,5,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

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

func (*BatchPredictRequest) Descriptor Uses

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

Deprecated: Use BatchPredictRequest.ProtoReflect.Descriptor instead.

func (*BatchPredictRequest) GetInputConfig Uses

func (x *BatchPredictRequest) GetInputConfig() *BatchPredictInputConfig

func (*BatchPredictRequest) GetName Uses

func (x *BatchPredictRequest) GetName() string

func (*BatchPredictRequest) GetOutputConfig Uses

func (x *BatchPredictRequest) GetOutputConfig() *BatchPredictOutputConfig

func (*BatchPredictRequest) GetParams Uses

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

func (*BatchPredictRequest) ProtoMessage Uses

func (*BatchPredictRequest) ProtoMessage()

func (*BatchPredictRequest) ProtoReflect Uses

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

func (*BatchPredictRequest) Reset Uses

func (x *BatchPredictRequest) Reset()

func (*BatchPredictRequest) String Uses

func (x *BatchPredictRequest) String() string

type BatchPredictResult Uses

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 `protobuf:"bytes,1,rep,name=metadata,proto3" json:"metadata,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // 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 Uses

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

Deprecated: Use BatchPredictResult.ProtoReflect.Descriptor instead.

func (*BatchPredictResult) GetMetadata Uses

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

func (*BatchPredictResult) ProtoMessage Uses

func (*BatchPredictResult) ProtoMessage()

func (*BatchPredictResult) ProtoReflect Uses

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

func (*BatchPredictResult) Reset Uses

func (x *BatchPredictResult) Reset()

func (*BatchPredictResult) String Uses

func (x *BatchPredictResult) String() string

type BoundingBoxMetricsEntry Uses

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 `protobuf:"bytes,3,rep,name=confidence_metrics_entries,json=confidenceMetricsEntries,proto3" json:"confidence_metrics_entries,omitempty"`
    // 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 Uses

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

Deprecated: Use BoundingBoxMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry) GetConfidenceMetricsEntries Uses

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

func (*BoundingBoxMetricsEntry) GetIouThreshold Uses

func (x *BoundingBoxMetricsEntry) GetIouThreshold() float32

func (*BoundingBoxMetricsEntry) GetMeanAveragePrecision Uses

func (x *BoundingBoxMetricsEntry) GetMeanAveragePrecision() float32

func (*BoundingBoxMetricsEntry) ProtoMessage Uses

func (*BoundingBoxMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry) ProtoReflect Uses

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

func (*BoundingBoxMetricsEntry) Reset Uses

func (x *BoundingBoxMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry) String Uses

func (x *BoundingBoxMetricsEntry) String() string

type BoundingBoxMetricsEntry_ConfidenceMetricsEntry Uses

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 Uses

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

Deprecated: Use BoundingBoxMetricsEntry_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetF1Score() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetPrecision() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) GetRecall() float32

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage Uses

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoMessage()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) ProtoReflect Uses

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

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) Reset()

func (*BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String Uses

func (x *BoundingBoxMetricsEntry_ConfidenceMetricsEntry) String() string

type BoundingPoly Uses

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 Uses

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

Deprecated: Use BoundingPoly.ProtoReflect.Descriptor instead.

func (*BoundingPoly) GetNormalizedVertices Uses

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

func (*BoundingPoly) ProtoMessage Uses

func (*BoundingPoly) ProtoMessage()

func (*BoundingPoly) ProtoReflect Uses

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

func (*BoundingPoly) Reset Uses

func (x *BoundingPoly) Reset()

func (*BoundingPoly) String Uses

func (x *BoundingPoly) String() string

type ClassificationAnnotation Uses

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 Uses

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

Deprecated: Use ClassificationAnnotation.ProtoReflect.Descriptor instead.

func (*ClassificationAnnotation) GetScore Uses

func (x *ClassificationAnnotation) GetScore() float32

func (*ClassificationAnnotation) ProtoMessage Uses

func (*ClassificationAnnotation) ProtoMessage()

func (*ClassificationAnnotation) ProtoReflect Uses

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

func (*ClassificationAnnotation) Reset Uses

func (x *ClassificationAnnotation) Reset()

func (*ClassificationAnnotation) String Uses

func (x *ClassificationAnnotation) String() string

type ClassificationEvaluationMetrics Uses

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 `protobuf:"bytes,3,rep,name=confidence_metrics_entry,json=confidenceMetricsEntry,proto3" json:"confidence_metrics_entry,omitempty"`
    // 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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics) GetAnnotationSpecId Uses

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

func (*ClassificationEvaluationMetrics) GetAuPrc Uses

func (x *ClassificationEvaluationMetrics) GetAuPrc() float32

func (*ClassificationEvaluationMetrics) GetAuRoc Uses

func (x *ClassificationEvaluationMetrics) GetAuRoc() float32

func (*ClassificationEvaluationMetrics) GetConfidenceMetricsEntry Uses

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

func (*ClassificationEvaluationMetrics) GetConfusionMatrix Uses

func (x *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix

func (*ClassificationEvaluationMetrics) GetLogLoss Uses

func (x *ClassificationEvaluationMetrics) GetLogLoss() float32

func (*ClassificationEvaluationMetrics) ProtoMessage Uses

func (*ClassificationEvaluationMetrics) ProtoMessage()

func (*ClassificationEvaluationMetrics) ProtoReflect Uses

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

func (*ClassificationEvaluationMetrics) Reset Uses

func (x *ClassificationEvaluationMetrics) Reset()

func (*ClassificationEvaluationMetrics) String Uses

func (x *ClassificationEvaluationMetrics) String() string

type ClassificationEvaluationMetrics_ConfidenceMetricsEntry Uses

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 `protobuf:"fixed32,9,opt,name=false_positive_rate_at1,json=falsePositiveRateAt1,proto3" json:"false_positive_rate_at1,omitempty"`
    // 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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalseNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRate() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetFalsePositiveRateAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPositionThreshold() int32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1 Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTrueNegativeCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetTruePositiveCount() int64

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage Uses

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoReflect Uses

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

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset()

func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String Uses

func (x *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string

type ClassificationEvaluationMetrics_ConfusionMatrix Uses

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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetDisplayName Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) GetRow Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage Uses

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoReflect Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix) Reset Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix) String Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix) String() string

type ClassificationEvaluationMetrics_ConfusionMatrix_Row Uses

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 Uses

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

Deprecated: Use ClassificationEvaluationMetrics_ConfusionMatrix_Row.ProtoReflect.Descriptor instead.

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage Uses

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoReflect Uses

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

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset()

func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) String Uses

func (x *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string

type ClassificationType Uses

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 Uses

func (ClassificationType) Descriptor() protoreflect.EnumDescriptor

func (ClassificationType) Enum Uses

func (x ClassificationType) Enum() *ClassificationType

func (ClassificationType) EnumDescriptor Uses

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

Deprecated: Use ClassificationType.Descriptor instead.

func (ClassificationType) Number Uses

func (x ClassificationType) Number() protoreflect.EnumNumber

func (ClassificationType) String Uses

func (x ClassificationType) String() string

func (ClassificationType) Type Uses

func (ClassificationType) Type() protoreflect.EnumType

type CreateDatasetOperationMetadata Uses

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

Details of CreateDataset operation.

func (*CreateDatasetOperationMetadata) Descriptor Uses

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

Deprecated: Use CreateDatasetOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateDatasetOperationMetadata) ProtoMessage Uses

func (*CreateDatasetOperationMetadata) ProtoMessage()

func (*CreateDatasetOperationMetadata) ProtoReflect Uses

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

func (*CreateDatasetOperationMetadata) Reset Uses

func (x *CreateDatasetOperationMetadata) Reset()

func (*CreateDatasetOperationMetadata) String Uses

func (x *CreateDatasetOperationMetadata) String() string

type CreateDatasetRequest Uses

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 Uses

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

Deprecated: Use CreateDatasetRequest.ProtoReflect.Descriptor instead.

func (*CreateDatasetRequest) GetDataset Uses

func (x *CreateDatasetRequest) GetDataset() *Dataset

func (*CreateDatasetRequest) GetParent Uses

func (x *CreateDatasetRequest) GetParent() string

func (*CreateDatasetRequest) ProtoMessage Uses

func (*CreateDatasetRequest) ProtoMessage()

func (*CreateDatasetRequest) ProtoReflect Uses

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

func (*CreateDatasetRequest) Reset Uses

func (x *CreateDatasetRequest) Reset()

func (*CreateDatasetRequest) String Uses

func (x *CreateDatasetRequest) String() string

type CreateModelOperationMetadata Uses

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

Details of CreateModel operation.

func (*CreateModelOperationMetadata) Descriptor Uses

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

Deprecated: Use CreateModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*CreateModelOperationMetadata) ProtoMessage Uses

func (*CreateModelOperationMetadata) ProtoMessage()

func (*CreateModelOperationMetadata) ProtoReflect Uses

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

func (*CreateModelOperationMetadata) Reset Uses

func (x *CreateModelOperationMetadata) Reset()

func (*CreateModelOperationMetadata) String Uses

func (x *CreateModelOperationMetadata) String() string

type CreateModelRequest Uses

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 Uses

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

Deprecated: Use CreateModelRequest.ProtoReflect.Descriptor instead.

func (*CreateModelRequest) GetModel Uses

func (x *CreateModelRequest) GetModel() *Model

func (*CreateModelRequest) GetParent Uses

func (x *CreateModelRequest) GetParent() string

func (*CreateModelRequest) ProtoMessage Uses

func (*CreateModelRequest) ProtoMessage()

func (*CreateModelRequest) ProtoReflect Uses

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

func (*CreateModelRequest) Reset Uses

func (x *CreateModelRequest) Reset()

func (*CreateModelRequest) String Uses

func (x *CreateModelRequest) String() string

type Dataset Uses

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 *timestamp.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 `protobuf:"bytes,39,rep,name=labels,proto3" json:"labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // 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 Uses

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

Deprecated: Use Dataset.ProtoReflect.Descriptor instead.

func (*Dataset) GetCreateTime Uses

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

func (*Dataset) GetDatasetMetadata Uses

func (m *Dataset) GetDatasetMetadata() isDataset_DatasetMetadata

func (*Dataset) GetDescription Uses

func (x *Dataset) GetDescription() string

func (*Dataset) GetDisplayName Uses

func (x *Dataset) GetDisplayName() string

func (*Dataset) GetEtag Uses

func (x *Dataset) GetEtag() string

func (*Dataset) GetExampleCount Uses

func (x *Dataset) GetExampleCount() int32

func (*Dataset) GetImageClassificationDatasetMetadata Uses

func (x *Dataset) GetImageClassificationDatasetMetadata() *ImageClassificationDatasetMetadata

func (*Dataset) GetImageObjectDetectionDatasetMetadata Uses

func (x *Dataset) GetImageObjectDetectionDatasetMetadata() *ImageObjectDetectionDatasetMetadata

func (*Dataset) GetLabels Uses

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

func (*Dataset) GetName Uses

func (x *Dataset) GetName() string

func (*Dataset) GetTextClassificationDatasetMetadata Uses

func (x *Dataset) GetTextClassificationDatasetMetadata() *TextClassificationDatasetMetadata

func (*Dataset) GetTextExtractionDatasetMetadata Uses

func (x *Dataset) GetTextExtractionDatasetMetadata() *TextExtractionDatasetMetadata

func (*Dataset) GetTextSentimentDatasetMetadata Uses

func (x *Dataset) GetTextSentimentDatasetMetadata() *TextSentimentDatasetMetadata

func (*Dataset) GetTranslationDatasetMetadata Uses

func (x *Dataset) GetTranslationDatasetMetadata() *TranslationDatasetMetadata

func (*Dataset) ProtoMessage Uses

func (*Dataset) ProtoMessage()

func (*Dataset) ProtoReflect Uses

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

func (*Dataset) Reset Uses

func (x *Dataset) Reset()

func (*Dataset) String Uses

func (x *Dataset) String() string

type Dataset_ImageClassificationDatasetMetadata Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

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 Uses

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

Deprecated: Use DeleteDatasetRequest.ProtoReflect.Descriptor instead.

func (*DeleteDatasetRequest) GetName Uses

func (x *DeleteDatasetRequest) GetName() string

func (*DeleteDatasetRequest) ProtoMessage Uses

func (*DeleteDatasetRequest) ProtoMessage()

func (*DeleteDatasetRequest) ProtoReflect Uses

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

func (*DeleteDatasetRequest) Reset Uses

func (x *DeleteDatasetRequest) Reset()

func (*DeleteDatasetRequest) String Uses

func (x *DeleteDatasetRequest) String() string

type DeleteModelRequest Uses

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 Uses

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

Deprecated: Use DeleteModelRequest.ProtoReflect.Descriptor instead.

func (*DeleteModelRequest) GetName Uses

func (x *DeleteModelRequest) GetName() string

func (*DeleteModelRequest) ProtoMessage Uses

func (*DeleteModelRequest) ProtoMessage()

func (*DeleteModelRequest) ProtoReflect Uses

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

func (*DeleteModelRequest) Reset Uses

func (x *DeleteModelRequest) Reset()

func (*DeleteModelRequest) String Uses

func (x *DeleteModelRequest) String() string

type DeleteOperationMetadata Uses

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

Details of operations that perform deletes of any entities.

func (*DeleteOperationMetadata) Descriptor Uses

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

Deprecated: Use DeleteOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeleteOperationMetadata) ProtoMessage Uses

func (*DeleteOperationMetadata) ProtoMessage()

func (*DeleteOperationMetadata) ProtoReflect Uses

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

func (*DeleteOperationMetadata) Reset Uses

func (x *DeleteOperationMetadata) Reset()

func (*DeleteOperationMetadata) String Uses

func (x *DeleteOperationMetadata) String() string

type DeployModelOperationMetadata Uses

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

Details of DeployModel operation.

func (*DeployModelOperationMetadata) Descriptor Uses

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

Deprecated: Use DeployModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*DeployModelOperationMetadata) ProtoMessage Uses

func (*DeployModelOperationMetadata) ProtoMessage()

func (*DeployModelOperationMetadata) ProtoReflect Uses

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

func (*DeployModelOperationMetadata) Reset Uses

func (x *DeployModelOperationMetadata) Reset()

func (*DeployModelOperationMetadata) String Uses

func (x *DeployModelOperationMetadata) String() string

type DeployModelRequest Uses

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 Uses

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

Deprecated: Use DeployModelRequest.ProtoReflect.Descriptor instead.

func (*DeployModelRequest) GetImageClassificationModelDeploymentMetadata Uses

func (x *DeployModelRequest) GetImageClassificationModelDeploymentMetadata() *ImageClassificationModelDeploymentMetadata

func (*DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata Uses

func (x *DeployModelRequest) GetImageObjectDetectionModelDeploymentMetadata() *ImageObjectDetectionModelDeploymentMetadata

func (*DeployModelRequest) GetModelDeploymentMetadata Uses

func (m *DeployModelRequest) GetModelDeploymentMetadata() isDeployModelRequest_ModelDeploymentMetadata

func (*DeployModelRequest) GetName Uses

func (x *DeployModelRequest) GetName() string

func (*DeployModelRequest) ProtoMessage Uses

func (*DeployModelRequest) ProtoMessage()

func (*DeployModelRequest) ProtoReflect Uses

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

func (*DeployModelRequest) Reset Uses

func (x *DeployModelRequest) Reset()

func (*DeployModelRequest) String Uses

func (x *DeployModelRequest) String() string

type DeployModelRequest_ImageClassificationModelDeploymentMetadata Uses

type DeployModelRequest_ImageClassificationModelDeploymentMetadata struct {
    // Model deployment metadata specific to Image Classification.
    ImageClassificationModelDeploymentMetadata *ImageClassificationModelDeploymentMetadata `protobuf:"bytes,4,opt,name=image_classification_model_deployment_metadata,json=imageClassificationModelDeploymentMetadata,proto3,oneof"`
}

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata Uses

type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata struct {
    // Model deployment metadata specific to Image Object Detection.
    ImageObjectDetectionModelDeploymentMetadata *ImageObjectDetectionModelDeploymentMetadata `protobuf:"bytes,2,opt,name=image_object_detection_model_deployment_metadata,json=imageObjectDetectionModelDeploymentMetadata,proto3,oneof"`
}

type Document Uses

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 Uses

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

Deprecated: Use Document.ProtoReflect.Descriptor instead.

func (*Document) GetDocumentDimensions Uses

func (x *Document) GetDocumentDimensions() *DocumentDimensions

func (*Document) GetDocumentText Uses

func (x *Document) GetDocumentText() *TextSnippet

func (*Document) GetInputConfig Uses

func (x *Document) GetInputConfig() *DocumentInputConfig

func (*Document) GetLayout Uses

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

func (*Document) GetPageCount Uses

func (x *Document) GetPageCount() int32

func (*Document) ProtoMessage Uses

func (*Document) ProtoMessage()

func (*Document) ProtoReflect Uses

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

func (*Document) Reset Uses

func (x *Document) Reset()

func (*Document) String Uses

func (x *Document) String() string

type DocumentDimensions Uses

type DocumentDimensions struct {

    // Unit of the dimension.
    Unit DocumentDimensions_DocumentDimensionUnit `protobuf:"varint,1,opt,name=unit,proto3,enum=google.cloud.automl.v1.DocumentDimensions_DocumentDimensionUnit" json:"unit,omitempty"`
    // 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 Uses

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

Deprecated: Use DocumentDimensions.ProtoReflect.Descriptor instead.

func (*DocumentDimensions) GetHeight Uses

func (x *DocumentDimensions) GetHeight() float32

func (*DocumentDimensions) GetUnit Uses

func (x *DocumentDimensions) GetUnit() DocumentDimensions_DocumentDimensionUnit

func (*DocumentDimensions) GetWidth Uses

func (x *DocumentDimensions) GetWidth() float32

func (*DocumentDimensions) ProtoMessage Uses

func (*DocumentDimensions) ProtoMessage()

func (*DocumentDimensions) ProtoReflect Uses

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

func (*DocumentDimensions) Reset Uses

func (x *DocumentDimensions) Reset()

func (*DocumentDimensions) String Uses

func (x *DocumentDimensions) String() string

type DocumentDimensions_DocumentDimensionUnit Uses

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 Uses

func (DocumentDimensions_DocumentDimensionUnit) Descriptor() protoreflect.EnumDescriptor

func (DocumentDimensions_DocumentDimensionUnit) Enum Uses

func (x DocumentDimensions_DocumentDimensionUnit) Enum() *DocumentDimensions_DocumentDimensionUnit

func (DocumentDimensions_DocumentDimensionUnit) EnumDescriptor Uses

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

Deprecated: Use DocumentDimensions_DocumentDimensionUnit.Descriptor instead.

func (DocumentDimensions_DocumentDimensionUnit) Number Uses

func (x DocumentDimensions_DocumentDimensionUnit) Number() protoreflect.EnumNumber

func (DocumentDimensions_DocumentDimensionUnit) String Uses

func (x DocumentDimensions_DocumentDimensionUnit) String() string

func (DocumentDimensions_DocumentDimensionUnit) Type Uses

func (DocumentDimensions_DocumentDimensionUnit) Type() protoreflect.EnumType

type DocumentInputConfig Uses

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 Uses

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

Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead.

func (*DocumentInputConfig) GetGcsSource Uses

func (x *DocumentInputConfig) GetGcsSource() *GcsSource

func (*DocumentInputConfig) ProtoMessage Uses

func (*DocumentInputConfig) ProtoMessage()

func (*DocumentInputConfig) ProtoReflect Uses

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

func (*DocumentInputConfig) Reset Uses

func (x *DocumentInputConfig) Reset()

func (*DocumentInputConfig) String Uses

func (x *DocumentInputConfig) String() string

type Document_Layout Uses

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 `protobuf:"varint,4,opt,name=text_segment_type,json=textSegmentType,proto3,enum=google.cloud.automl.v1.Document_Layout_TextSegmentType" json:"text_segment_type,omitempty"`
    // 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 Uses

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

Deprecated: Use Document_Layout.ProtoReflect.Descriptor instead.

func (*Document_Layout) GetBoundingPoly Uses

func (x *Document_Layout) GetBoundingPoly() *BoundingPoly

func (*Document_Layout) GetPageNumber Uses

func (x *Document_Layout) GetPageNumber() int32

func (*Document_Layout) GetTextSegment Uses

func (x *Document_Layout) GetTextSegment() *TextSegment

func (*Document_Layout) GetTextSegmentType Uses

func (x *Document_Layout) GetTextSegmentType() Document_Layout_TextSegmentType

func (*Document_Layout) ProtoMessage Uses

func (*Document_Layout) ProtoMessage()

func (*Document_Layout) ProtoReflect Uses

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

func (*Document_Layout) Reset Uses

func (x *Document_Layout) Reset()

func (*Document_Layout) String Uses

func (x *Document_Layout) String() string

type Document_Layout_TextSegmentType Uses

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 Uses

func (Document_Layout_TextSegmentType) Descriptor() protoreflect.EnumDescriptor

func (Document_Layout_TextSegmentType) Enum Uses

func (x Document_Layout_TextSegmentType) Enum() *Document_Layout_TextSegmentType

func (Document_Layout_TextSegmentType) EnumDescriptor Uses

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

Deprecated: Use Document_Layout_TextSegmentType.Descriptor instead.

func (Document_Layout_TextSegmentType) Number Uses

func (x Document_Layout_TextSegmentType) Number() protoreflect.EnumNumber

func (Document_Layout_TextSegmentType) String Uses

func (x Document_Layout_TextSegmentType) String() string

func (Document_Layout_TextSegmentType) Type Uses

func (Document_Layout_TextSegmentType) Type() protoreflect.EnumType

type ExamplePayload Uses

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 Uses

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

Deprecated: Use ExamplePayload.ProtoReflect.Descriptor instead.

func (*ExamplePayload) GetDocument Uses

func (x *ExamplePayload) GetDocument() *Document

func (*ExamplePayload) GetImage Uses

func (x *ExamplePayload) GetImage() *Image

func (*ExamplePayload) GetPayload Uses

func (m *ExamplePayload) GetPayload() isExamplePayload_Payload

func (*ExamplePayload) GetTextSnippet Uses

func (x *ExamplePayload) GetTextSnippet() *TextSnippet

func (*ExamplePayload) ProtoMessage Uses

func (*ExamplePayload) ProtoMessage()

func (*ExamplePayload) ProtoReflect Uses

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

func (*ExamplePayload) Reset Uses

func (x *ExamplePayload) Reset()

func (*ExamplePayload) String Uses

func (x *ExamplePayload) String() string

type ExamplePayload_Document Uses

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

type ExamplePayload_Image Uses

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

type ExamplePayload_TextSnippet Uses

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

type ExportDataOperationMetadata Uses

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 Uses

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

Deprecated: Use ExportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata) GetOutputInfo Uses

func (x *ExportDataOperationMetadata) GetOutputInfo() *ExportDataOperationMetadata_ExportDataOutputInfo

func (*ExportDataOperationMetadata) ProtoMessage Uses

func (*ExportDataOperationMetadata) ProtoMessage()

func (*ExportDataOperationMetadata) ProtoReflect Uses

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

func (*ExportDataOperationMetadata) Reset Uses

func (x *ExportDataOperationMetadata) Reset()

func (*ExportDataOperationMetadata) String Uses

func (x *ExportDataOperationMetadata) String() string

type ExportDataOperationMetadata_ExportDataOutputInfo Uses

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 Uses

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

Deprecated: Use ExportDataOperationMetadata_ExportDataOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) GetGcsOutputDirectory() string

func (*ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation Uses

func (m *ExportDataOperationMetadata_ExportDataOutputInfo) GetOutputLocation() isExportDataOperationMetadata_ExportDataOutputInfo_OutputLocation

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage Uses

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoMessage()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) ProtoReflect Uses

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

func (*ExportDataOperationMetadata_ExportDataOutputInfo) Reset Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) Reset()

func (*ExportDataOperationMetadata_ExportDataOutputInfo) String Uses

func (x *ExportDataOperationMetadata_ExportDataOutputInfo) String() string

type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory Uses

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 Uses

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 Uses

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

Deprecated: Use ExportDataRequest.ProtoReflect.Descriptor instead.

func (*ExportDataRequest) GetName Uses

func (x *ExportDataRequest) GetName() string

func (*ExportDataRequest) GetOutputConfig Uses

func (x *ExportDataRequest) GetOutputConfig() *OutputConfig

func (*ExportDataRequest) ProtoMessage Uses

func (*ExportDataRequest) ProtoMessage()

func (*ExportDataRequest) ProtoReflect Uses

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

func (*ExportDataRequest) Reset Uses

func (x *ExportDataRequest) Reset()

func (*ExportDataRequest) String Uses

func (x *ExportDataRequest) String() string

type ExportModelOperationMetadata Uses

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 Uses

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

Deprecated: Use ExportModelOperationMetadata.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata) GetOutputInfo Uses

func (x *ExportModelOperationMetadata) GetOutputInfo() *ExportModelOperationMetadata_ExportModelOutputInfo

func (*ExportModelOperationMetadata) ProtoMessage Uses

func (*ExportModelOperationMetadata) ProtoMessage()

func (*ExportModelOperationMetadata) ProtoReflect Uses

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

func (*ExportModelOperationMetadata) Reset Uses

func (x *ExportModelOperationMetadata) Reset()

func (*ExportModelOperationMetadata) String Uses

func (x *ExportModelOperationMetadata) String() string

type ExportModelOperationMetadata_ExportModelOutputInfo Uses

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 Uses

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

Deprecated: Use ExportModelOperationMetadata_ExportModelOutputInfo.ProtoReflect.Descriptor instead.

func (*ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) GetGcsOutputDirectory() string

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage Uses

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoMessage()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) ProtoReflect Uses

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

func (*ExportModelOperationMetadata_ExportModelOutputInfo) Reset Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) Reset()

func (*ExportModelOperationMetadata_ExportModelOutputInfo) String Uses

func (x *ExportModelOperationMetadata_ExportModelOutputInfo) String() string

type ExportModelRequest Uses

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 Uses

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

Deprecated: Use ExportModelRequest.ProtoReflect.Descriptor instead.

func (*ExportModelRequest) GetName Uses

func (x *ExportModelRequest) GetName() string

func (*ExportModelRequest) GetOutputConfig Uses

func (x *ExportModelRequest) GetOutputConfig() *ModelExportOutputConfig

func (*ExportModelRequest) ProtoMessage Uses

func (*ExportModelRequest) ProtoMessage()

func (*ExportModelRequest) ProtoReflect Uses

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

func (*ExportModelRequest) Reset Uses

func (x *ExportModelRequest) Reset()

func (*ExportModelRequest) String Uses

func (x *ExportModelRequest) String() string

type GcsDestination Uses

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 Uses

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

Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead.

func (*GcsDestination) GetOutputUriPrefix Uses

func (x *GcsDestination) GetOutputUriPrefix() string

func (*GcsDestination) ProtoMessage Uses

func (*GcsDestination) ProtoMessage()

func (*GcsDestination) ProtoReflect Uses

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

func (*GcsDestination) Reset Uses

func (x *GcsDestination) Reset()

func (*GcsDestination) String Uses

func (x *GcsDestination) String() string

type GcsSource Uses

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 Uses

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

Deprecated: Use GcsSource.ProtoReflect.Descriptor instead.

func (*GcsSource) GetInputUris Uses

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

func (*GcsSource) ProtoMessage Uses

func (*GcsSource) ProtoMessage()

func (*GcsSource) ProtoReflect Uses

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

func (*GcsSource) Reset Uses

func (x *GcsSource) Reset()

func (*GcsSource) String Uses

func (x *GcsSource) String() string

type GetAnnotationSpecRequest Uses

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 Uses

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

Deprecated: Use GetAnnotationSpecRequest.ProtoReflect.Descriptor instead.

func (*GetAnnotationSpecRequest) GetName Uses

func (x *GetAnnotationSpecRequest) GetName() string

func (*GetAnnotationSpecRequest) ProtoMessage Uses

func (*GetAnnotationSpecRequest) ProtoMessage()

func (*GetAnnotationSpecRequest) ProtoReflect Uses

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

func (*GetAnnotationSpecRequest) Reset Uses

func (x *GetAnnotationSpecRequest) Reset()

func (*GetAnnotationSpecRequest) String Uses

func (x *GetAnnotationSpecRequest) String() string

type GetDatasetRequest Uses

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 Uses

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

Deprecated: Use GetDatasetRequest.ProtoReflect.Descriptor instead.

func (*GetDatasetRequest) GetName Uses

func (x *GetDatasetRequest) GetName() string

func (*GetDatasetRequest) ProtoMessage Uses

func (*GetDatasetRequest) ProtoMessage()

func (*GetDatasetRequest) ProtoReflect Uses

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

func (*GetDatasetRequest) Reset Uses

func (x *GetDatasetRequest) Reset()

func (*GetDatasetRequest) String Uses

func (x *GetDatasetRequest) String() string

type GetModelEvaluationRequest Uses

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 Uses

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

Deprecated: Use GetModelEvaluationRequest.ProtoReflect.Descriptor instead.

func (*GetModelEvaluationRequest) GetName Uses

func (x *GetModelEvaluationRequest) GetName() string

func (*GetModelEvaluationRequest) ProtoMessage Uses

func (*GetModelEvaluationRequest) ProtoMessage()

func (*GetModelEvaluationRequest) ProtoReflect Uses

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

func (*GetModelEvaluationRequest) Reset Uses

func (x *GetModelEvaluationRequest) Reset()

func (*GetModelEvaluationRequest) String Uses

func (x *GetModelEvaluationRequest) String() string

type GetModelRequest Uses

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 Uses

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

Deprecated: Use GetModelRequest.ProtoReflect.Descriptor instead.

func (*GetModelRequest) GetName Uses

func (x *GetModelRequest) GetName() string

func (*GetModelRequest) ProtoMessage Uses

func (*GetModelRequest) ProtoMessage()

func (*GetModelRequest) ProtoReflect Uses

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

func (*GetModelRequest) Reset Uses

func (x *GetModelRequest) Reset()

func (*GetModelRequest) String Uses

func (x *GetModelRequest) String() string

type Image Uses

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 Uses

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

Deprecated: Use Image.ProtoReflect.Descriptor instead.

func (*Image) GetData Uses

func (m *Image) GetData() isImage_Data

func (*Image) GetImageBytes Uses

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

func (*Image) GetThumbnailUri Uses

func (x *Image) GetThumbnailUri() string

func (*Image) ProtoMessage Uses

func (*Image) ProtoMessage()

func (*Image) ProtoReflect Uses

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

func (*Image) Reset Uses

func (x *Image) Reset()

func (*Image) String Uses

func (x *Image) String() string

type ImageClassificationDatasetMetadata Uses

type ImageClassificationDatasetMetadata struct {

    // Required. Type of the classification problem.
    ClassificationType ClassificationType `protobuf:"varint,1,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1.ClassificationType" json:"classification_type,omitempty"`
    // contains filtered or unexported fields
}

Dataset metadata that is specific to image classification.

func (*ImageClassificationDatasetMetadata) Descriptor Uses

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

Deprecated: Use ImageClassificationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationDatasetMetadata) GetClassificationType Uses

func (x *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*ImageClassificationDatasetMetadata) ProtoMessage Uses

func (*ImageClassificationDatasetMetadata) ProtoMessage()

func (*ImageClassificationDatasetMetadata) ProtoReflect Uses

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

func (*ImageClassificationDatasetMetadata) Reset Uses

func (x *ImageClassificationDatasetMetadata) Reset()

func (*ImageClassificationDatasetMetadata) String Uses

func (x *ImageClassificationDatasetMetadata) String() string

type ImageClassificationModelDeploymentMetadata Uses

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 Uses

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

Deprecated: Use ImageClassificationModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelDeploymentMetadata) GetNodeCount Uses

func (x *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage Uses

func (*ImageClassificationModelDeploymentMetadata) ProtoMessage()

func (*ImageClassificationModelDeploymentMetadata) ProtoReflect Uses

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

func (*ImageClassificationModelDeploymentMetadata) Reset Uses

func (x *ImageClassificationModelDeploymentMetadata) Reset()

func (*ImageClassificationModelDeploymentMetadata) String Uses

func (x *ImageClassificationModelDeploymentMetadata) String() string

type ImageClassificationModelMetadata Uses

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 `protobuf:"varint,16,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
    // 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 `protobuf:"varint,17,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,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"`
    // 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 Uses

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

Deprecated: Use ImageClassificationModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageClassificationModelMetadata) GetBaseModelId Uses

func (x *ImageClassificationModelMetadata) GetBaseModelId() string

func (*ImageClassificationModelMetadata) GetModelType Uses

func (x *ImageClassificationModelMetadata) GetModelType() string

func (*ImageClassificationModelMetadata) GetNodeCount Uses

func (x *ImageClassificationModelMetadata) GetNodeCount() int64

func (*ImageClassificationModelMetadata) GetNodeQps Uses

func (x *ImageClassificationModelMetadata) GetNodeQps() float64

func (*ImageClassificationModelMetadata) GetStopReason Uses

func (x *ImageClassificationModelMetadata) GetStopReason() string

func (*ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours Uses

func (x *ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageClassificationModelMetadata) GetTrainCostMilliNodeHours Uses

func (x *ImageClassificationModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageClassificationModelMetadata) ProtoMessage Uses

func (*ImageClassificationModelMetadata) ProtoMessage()

func (*ImageClassificationModelMetadata) ProtoReflect Uses

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

func (*ImageClassificationModelMetadata) Reset Uses

func (x *ImageClassificationModelMetadata) Reset()

func (*ImageClassificationModelMetadata) String Uses

func (x *ImageClassificationModelMetadata) String() string

type ImageObjectDetectionAnnotation Uses

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 Uses

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

Deprecated: Use ImageObjectDetectionAnnotation.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionAnnotation) GetBoundingBox Uses

func (x *ImageObjectDetectionAnnotation) GetBoundingBox() *BoundingPoly

func (*ImageObjectDetectionAnnotation) GetScore Uses

func (x *ImageObjectDetectionAnnotation) GetScore() float32

func (*ImageObjectDetectionAnnotation) ProtoMessage Uses

func (*ImageObjectDetectionAnnotation) ProtoMessage()

func (*ImageObjectDetectionAnnotation) ProtoReflect Uses

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

func (*ImageObjectDetectionAnnotation) Reset Uses

func (x *ImageObjectDetectionAnnotation) Reset()

func (*ImageObjectDetectionAnnotation) String Uses

func (x *ImageObjectDetectionAnnotation) String() string

type ImageObjectDetectionDatasetMetadata Uses

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

Dataset metadata specific to image object detection.

func (*ImageObjectDetectionDatasetMetadata) Descriptor Uses

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

Deprecated: Use ImageObjectDetectionDatasetMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage Uses

func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()

func (*ImageObjectDetectionDatasetMetadata) ProtoReflect Uses

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

func (*ImageObjectDetectionDatasetMetadata) Reset Uses

func (x *ImageObjectDetectionDatasetMetadata) Reset()

func (*ImageObjectDetectionDatasetMetadata) String Uses

func (x *ImageObjectDetectionDatasetMetadata) String() string

type ImageObjectDetectionEvaluationMetrics Uses

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 `protobuf:"varint,1,opt,name=evaluated_bounding_box_count,json=evaluatedBoundingBoxCount,proto3" json:"evaluated_bounding_box_count,omitempty"`
    // 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 `protobuf:"bytes,2,rep,name=bounding_box_metrics_entries,json=boundingBoxMetricsEntries,proto3" json:"bounding_box_metrics_entries,omitempty"`
    // Output only. The single metric for bounding boxes evaluation:
    // the mean_average_precision averaged over all bounding_box_metrics_entries.
    BoundingBoxMeanAveragePrecision float32 `protobuf:"fixed32,3,opt,name=bounding_box_mean_average_precision,json=boundingBoxMeanAveragePrecision,proto3" json:"bounding_box_mean_average_precision,omitempty"`
    // contains filtered or unexported fields
}

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

func (*ImageObjectDetectionEvaluationMetrics) Descriptor Uses

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

Deprecated: Use ImageObjectDetectionEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision Uses

func (x *ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMeanAveragePrecision() float32

func (*ImageObjectDetectionEvaluationMetrics) GetBoundingBoxMetricsEntries Uses

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

func (*ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount Uses

func (x *ImageObjectDetectionEvaluationMetrics) GetEvaluatedBoundingBoxCount() int32

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage Uses

func (*ImageObjectDetectionEvaluationMetrics) ProtoMessage()

func (*ImageObjectDetectionEvaluationMetrics) ProtoReflect Uses

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

func (*ImageObjectDetectionEvaluationMetrics) Reset Uses

func (x *ImageObjectDetectionEvaluationMetrics) Reset()

func (*ImageObjectDetectionEvaluationMetrics) String Uses

func (x *ImageObjectDetectionEvaluationMetrics) String() string

type ImageObjectDetectionModelDeploymentMetadata Uses

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 Uses

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

Deprecated: Use ImageObjectDetectionModelDeploymentMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelDeploymentMetadata) GetNodeCount Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage Uses

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage()

func (*ImageObjectDetectionModelDeploymentMetadata) ProtoReflect Uses

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

func (*ImageObjectDetectionModelDeploymentMetadata) Reset Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) Reset()

func (*ImageObjectDetectionModelDeploymentMetadata) String Uses

func (x *ImageObjectDetectionModelDeploymentMetadata) String() string

type ImageObjectDetectionModelMetadata Uses

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 `protobuf:"varint,6,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
    // 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 `protobuf:"varint,7,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"`
    // contains filtered or unexported fields
}

Model metadata specific to image object detection.

func (*ImageObjectDetectionModelMetadata) Descriptor Uses

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

Deprecated: Use ImageObjectDetectionModelMetadata.ProtoReflect.Descriptor instead.

func (*ImageObjectDetectionModelMetadata) GetModelType Uses

func (x *ImageObjectDetectionModelMetadata) GetModelType() string

func (*ImageObjectDetectionModelMetadata) GetNodeCount Uses

func (x *ImageObjectDetectionModelMetadata) GetNodeCount() int64

func (*ImageObjectDetectionModelMetadata) GetNodeQps Uses

func (x *ImageObjectDetectionModelMetadata) GetNodeQps() float64

func (*ImageObjectDetectionModelMetadata) GetStopReason Uses

func (x *ImageObjectDetectionModelMetadata) GetStopReason() string

func (*ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours Uses

func (x *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours Uses

func (x *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64

func (*ImageObjectDetectionModelMetadata) ProtoMessage Uses

func (*ImageObjectDetectionModelMetadata) ProtoMessage()

func (*ImageObjectDetectionModelMetadata) ProtoReflect Uses

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

func (*ImageObjectDetectionModelMetadata) Reset Uses

func (x *ImageObjectDetectionModelMetadata) Reset()

func (*ImageObjectDetectionModelMetadata) String Uses

func (x *ImageObjectDetectionModelMetadata) String() string

type Image_ImageBytes Uses

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 Uses

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

Details of ImportData operation.

func (*ImportDataOperationMetadata) Descriptor Uses

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

Deprecated: Use ImportDataOperationMetadata.ProtoReflect.Descriptor instead.

func (*ImportDataOperationMetadata) ProtoMessage Uses

func (*ImportDataOperationMetadata) ProtoMessage()

func (*ImportDataOperationMetadata) ProtoReflect Uses

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

func (*ImportDataOperationMetadata) Reset Uses

func (x *ImportDataOperationMetadata) Reset()

func (*ImportDataOperationMetadata) String Uses

func (x *ImportDataOperationMetadata) String() string

type ImportDataRequest Uses

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 Uses

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

Deprecated: Use ImportDataRequest.ProtoReflect.Descriptor instead.

func (*ImportDataRequest) GetInputConfig Uses

func (x *ImportDataRequest) GetInputConfig() *InputConfig

func (*ImportDataRequest) GetName Uses

func (x *ImportDataRequest) GetName() string

func (*ImportDataRequest) ProtoMessage Uses

func (*ImportDataRequest) ProtoMessage()

func (*ImportDataRequest) ProtoReflect Uses

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

func (*ImportDataRequest) Reset Uses

func (x *ImportDataRequest) Reset()

func (*ImportDataRequest) String Uses

func (x *ImportDataRequest) String() string

type InputConfig Uses

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 `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // 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 Uses

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

Deprecated: Use InputConfig.ProtoReflect.Descriptor instead.

func (*InputConfig) GetGcsSource Uses

func (x *InputConfig) GetGcsSource() *GcsSource

func (*InputConfig) GetParams Uses

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

func (*InputConfig) GetSource Uses

func (m *InputConfig) GetSource() isInputConfig_Source

func (*InputConfig) ProtoMessage Uses

func (*InputConfig) ProtoMessage()

func (*InputConfig) ProtoReflect Uses

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

func (*InputConfig) Reset Uses

func (x *InputConfig) Reset()

func (*InputConfig) String Uses

func (x *InputConfig) String() string

type InputConfig_GcsSource Uses

type InputConfig_GcsSource struct {
    // The Google Cloud Storage location for the input content.
    // For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
    // a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
    GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

type ListDatasetsRequest Uses

type ListDatasetsRequest struct {

    // Required. The resource name of the project from which to list datasets.
    Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
    // An expression for filtering the results of the request.
    //
    //   * `dataset_metadata` - for existence of the case (e.g.
    //             image_classification_dataset_metadata:*). Some examples of using the filter are:
    //
    //   * `translation_dataset_metadata:*` --> The dataset has
    //                                          translation_dataset_metadata.
    Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
    // Requested page size. Server may return fewer results than requested.
    // If unspecified, server will pick a default size.
    PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
    // A token identifying a page of results for the server to return
    // Typically obtained via
    // [ListDatasetsResponse.next_page_token][google.cloud.automl.v1.ListDatasetsResponse.next_page_token] of the previous
    // [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets] call.
    PageToken string `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
    // contains filtered or unexported fields
}

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

func (*ListDatasetsRequest) Descriptor Uses

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

Deprecated: Use ListDatasetsRequest.ProtoReflect.Descriptor instead.

func (*ListDatasetsRequest) GetFilter Uses

func (x *ListDatasetsRequest) GetFilter() string

func (*ListDatasetsRequest) GetPageSize Uses

func (x *ListDatasetsRequest) GetPageSize() int32

func (*ListDatasetsRequest) GetPageToken Uses

func (x *ListDatasetsRequest) GetPageToken() string

func (*ListDatasetsRequest) GetParent Uses

func (x *ListDatasetsRequest) GetParent() string

func (*ListDatasetsRequest) ProtoMessage Uses

func (*ListDatasetsRequest) ProtoMessage()

func (*ListDatasetsRequest) ProtoReflect Uses

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

func (*ListDatasetsRequest) Reset Uses

func (x *ListDatasetsRequest) Reset()

func (*ListDatasetsRequest) String Uses

func (x *ListDatasetsRequest) String() string

type ListDatasetsResponse Uses

type ListDatasetsResponse struct {

    // The datasets read.
    Datasets []*Dataset `protobuf:"bytes,1,rep,name=datasets,proto3" json:"datasets,omitempty"`
    // A token to retrieve next page of results.
    // Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1.ListDatasetsRequest.page_token] to obtain that page.
    NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
    // contains filtered or unexported fields
}

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

func (*ListDatasetsResponse) Descriptor Uses

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

Deprecated: Use ListDatasetsResponse.ProtoReflect.Descriptor instead.

func (*ListDatasetsResponse) GetDatasets Uses

func (x *ListDatasetsResponse) GetDatasets() []*Dataset

func (*ListDatasetsResponse) GetNextPageToken Uses

func (x *ListDatasetsResponse) GetNextPageToken() string

func (*ListDatasetsResponse) ProtoMessage Uses

func (*ListDatasetsResponse) ProtoMessage()

func (*ListDatasetsResponse) ProtoReflect Uses

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

func (*ListDatasetsResponse) Reset Uses

func (x *ListDatasetsResponse) Reset()

func (*ListDatasetsResponse) String Uses

func (x *ListDatasetsResponse) String() string

type ListModelEvaluationsRequest Uses

type ListModelEvaluationsRequest struct {

    // Required. Resource name of the model to list the model evaluations for.
    // If modelId is set as "-", this will list model evaluations from across all
    // models of the parent location.
    Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
    // Required. An expression for filtering the results of the request.
    //
    //   * `annotation_spec_id` - for =, !=  or existence. See example below for
    //                          the last.
    //
    // Some examples of using the filter are:
    //
    //   * `annotation_spec_id!=4` --> The model evaluation was done for
    //                             annotation spec with ID different than 4.
    //   * `NOT annotation_spec_id:*` --> The model evaluation was done for
    //                                aggregate of all annotation specs.
    Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
    // Requested page size.
    PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
    // A token identifying a page of results for the server to return.
    // Typically obtained via
    // [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1.ListModelEvaluationsResponse.next_page_token] of the previous
    // [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] call.
    PageToken string `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
    // contains filtered or unexported fields
}

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

func (*ListModelEvaluationsRequest) Descriptor Uses

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

Deprecated: Use ListModelEvaluationsRequest.ProtoReflect.Descriptor instead.

func (*ListModelEvaluationsRequest) GetFilter Uses

func (x *ListModelEvaluationsRequest) GetFilter() string

func (*ListModelEvaluationsRequest) GetPageSize Uses

func (x *ListModelEvaluationsRequest) GetPageSize() int32

func (*ListModelEvaluationsRequest) GetPageToken Uses

func (x *ListModelEvaluationsRequest) GetPageToken() string

func (*ListModelEvaluationsRequest) GetParent Uses

func (x *ListModelEvaluationsRequest) GetParent() string

func (*ListModelEvaluationsRequest) ProtoMessage Uses

func (*ListModelEvaluationsRequest) ProtoMessage()

func (*ListModelEvaluationsRequest) ProtoReflect Uses

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

func (*ListModelEvaluationsRequest) Reset Uses

func (x *ListModelEvaluationsRequest) Reset()

func (*ListModelEvaluationsRequest) String Uses

func (x *ListModelEvaluationsRequest) String() string

type ListModelEvaluationsResponse Uses

type ListModelEvaluationsResponse struct {

    // List of model evaluations in the requested page.
    ModelEvaluation []*ModelEvaluation `protobuf:"bytes,1,rep,name=model_evaluation,json=modelEvaluation,proto3" json:"model_evaluation,omitempty"`
    // A token to retrieve next page of results.
    // Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1.ListModelEvaluationsRequest.page_token] field of a new
    // [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] request to obtain that page.
    NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
    // contains filtered or unexported fields
}

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

func (*ListModelEvaluationsResponse) Descriptor Uses

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

Deprecated: Use ListModelEvaluationsResponse.ProtoReflect.Descriptor instead.

func (*ListModelEvaluationsResponse) GetModelEvaluation Uses

func (x *ListModelEvaluationsResponse) GetModelEvaluation() []*ModelEvaluation

func (*ListModelEvaluationsResponse) GetNextPageToken Uses

func (x *ListModelEvaluationsResponse) GetNextPageToken() string

func (*ListModelEvaluationsResponse) ProtoMessage Uses

func (*ListModelEvaluationsResponse) ProtoMessage()

func (*ListModelEvaluationsResponse) ProtoReflect Uses

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

func (*ListModelEvaluationsResponse) Reset Uses

func (x *ListModelEvaluationsResponse) Reset()

func (*ListModelEvaluationsResponse) String Uses

func (x *ListModelEvaluationsResponse) String() string

type ListModelsRequest Uses

type ListModelsRequest struct {

    // Required. Resource name of the project, from which to list the models.
    Parent string `protobuf:"bytes,1,opt,name=parent,proto3" json:"parent,omitempty"`
    // An expression for filtering the results of the request.
    //
    //   * `model_metadata` - for existence of the case (e.g.
    //             video_classification_model_metadata:*).
    //   * `dataset_id` - for = or !=. Some examples of using the filter are:
    //
    //   * `image_classification_model_metadata:*` --> The model has
    //                                        image_classification_model_metadata.
    //   * `dataset_id=5` --> The model was created from a dataset with ID 5.
    Filter string `protobuf:"bytes,3,opt,name=filter,proto3" json:"filter,omitempty"`
    // Requested page size.
    PageSize int32 `protobuf:"varint,4,opt,name=page_size,json=pageSize,proto3" json:"page_size,omitempty"`
    // A token identifying a page of results for the server to return
    // Typically obtained via
    // [ListModelsResponse.next_page_token][google.cloud.automl.v1.ListModelsResponse.next_page_token] of the previous
    // [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels] call.
    PageToken string `protobuf:"bytes,6,opt,name=page_token,json=pageToken,proto3" json:"page_token,omitempty"`
    // contains filtered or unexported fields
}

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

func (*ListModelsRequest) Descriptor Uses

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

Deprecated: Use ListModelsRequest.ProtoReflect.Descriptor instead.

func (*ListModelsRequest) GetFilter Uses

func (x *ListModelsRequest) GetFilter() string

func (*ListModelsRequest) GetPageSize Uses

func (x *ListModelsRequest) GetPageSize() int32

func (*ListModelsRequest) GetPageToken Uses

func (x *ListModelsRequest) GetPageToken() string

func (*ListModelsRequest) GetParent Uses

func (x *ListModelsRequest) GetParent() string

func (*ListModelsRequest) ProtoMessage Uses

func (*ListModelsRequest) ProtoMessage()

func (*ListModelsRequest) ProtoReflect Uses

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

func (*ListModelsRequest) Reset Uses

func (x *ListModelsRequest) Reset()

func (*ListModelsRequest) String Uses

func (x *ListModelsRequest) String() string

type ListModelsResponse Uses

type ListModelsResponse struct {

    // List of models in the requested page.
    Model []*Model `protobuf:"bytes,1,rep,name=model,proto3" json:"model,omitempty"`
    // A token to retrieve next page of results.
    // Pass to [ListModelsRequest.page_token][google.cloud.automl.v1.ListModelsRequest.page_token] to obtain that page.
    NextPageToken string `protobuf:"bytes,2,opt,name=next_page_token,json=nextPageToken,proto3" json:"next_page_token,omitempty"`
    // contains filtered or unexported fields
}

Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

func (*ListModelsResponse) Descriptor Uses

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

Deprecated: Use ListModelsResponse.ProtoReflect.Descriptor instead.

func (*ListModelsResponse) GetModel Uses

func (x *ListModelsResponse) GetModel() []*Model

func (*ListModelsResponse) GetNextPageToken Uses

func (x *ListModelsResponse) GetNextPageToken() string

func (*ListModelsResponse) ProtoMessage Uses

func (*ListModelsResponse) ProtoMessage()

func (*ListModelsResponse) ProtoReflect Uses

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

func (*ListModelsResponse) Reset Uses

func (x *ListModelsResponse) Reset()

func (*ListModelsResponse) String Uses

func (x *ListModelsResponse) String() string

type Model Uses

type Model struct {

    // Required.
    // The model metadata that is specific to the problem type.
    // Must match the metadata type of the dataset used to train the model.
    //
    // Types that are assignable to ModelMetadata:
    //	*Model_TranslationModelMetadata
    //	*Model_ImageClassificationModelMetadata
    //	*Model_TextClassificationModelMetadata
    //	*Model_ImageObjectDetectionModelMetadata
    //	*Model_TextExtractionModelMetadata
    //	*Model_TextSentimentModelMetadata
    ModelMetadata isModel_ModelMetadata `protobuf_oneof:"model_metadata"`
    // Output only. Resource name of the model.
    // Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
    Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
    // Required. The name of the model 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. It must start with a letter.
    DisplayName string `protobuf:"bytes,2,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
    // Required. The resource ID of the dataset used to create the model. The dataset must
    // come from the same ancestor project and location.
    DatasetId string `protobuf:"bytes,3,opt,name=dataset_id,json=datasetId,proto3" json:"dataset_id,omitempty"`
    // Output only. Timestamp when the model training finished  and can be used for prediction.
    CreateTime *timestamp.Timestamp `protobuf:"bytes,7,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
    // Output only. Timestamp when this model was last updated.
    UpdateTime *timestamp.Timestamp `protobuf:"bytes,11,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
    // Output only. Deployment state of the model. A model can only serve
    // prediction requests after it gets deployed.
    DeploymentState Model_DeploymentState `protobuf:"varint,8,opt,name=deployment_state,json=deploymentState,proto3,enum=google.cloud.automl.v1.Model_DeploymentState" json:"deployment_state,omitempty"`
    // Used to perform a consistent read-modify-write updates. If not set, a blind
    // "overwrite" update happens.
    Etag string `protobuf:"bytes,10,opt,name=etag,proto3" json:"etag,omitempty"`
    // Optional. The labels with user-defined metadata to organize your model.
    //
    // 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 `protobuf:"bytes,34,rep,name=labels,proto3" json:"labels,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

API proto representing a trained machine learning model.

func (*Model) Descriptor Uses

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

Deprecated: Use Model.ProtoReflect.Descriptor instead.

func (*Model) GetCreateTime Uses

func (x *Model) GetCreateTime() *timestamp.Timestamp

func (*Model) GetDatasetId Uses

func (x *Model) GetDatasetId() string

func (*Model) GetDeploymentState Uses

func (x *Model) GetDeploymentState() Model_DeploymentState

func (*Model) GetDisplayName Uses

func (x *Model) GetDisplayName() string

func (*Model) GetEtag Uses

func (x *Model) GetEtag() string

func (*Model) GetImageClassificationModelMetadata Uses

func (x *Model) GetImageClassificationModelMetadata() *ImageClassificationModelMetadata

func (*Model) GetImageObjectDetectionModelMetadata Uses

func (x *Model) GetImageObjectDetectionModelMetadata() *ImageObjectDetectionModelMetadata

func (*Model) GetLabels Uses

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

func (*Model) GetModelMetadata Uses

func (m *Model) GetModelMetadata() isModel_ModelMetadata

func (*Model) GetName Uses

func (x *Model) GetName() string

func (*Model) GetTextClassificationModelMetadata Uses

func (x *Model) GetTextClassificationModelMetadata() *TextClassificationModelMetadata

func (*Model) GetTextExtractionModelMetadata Uses

func (x *Model) GetTextExtractionModelMetadata() *TextExtractionModelMetadata

func (*Model) GetTextSentimentModelMetadata Uses

func (x *Model) GetTextSentimentModelMetadata() *TextSentimentModelMetadata

func (*Model) GetTranslationModelMetadata Uses

func (x *Model) GetTranslationModelMetadata() *TranslationModelMetadata

func (*Model) GetUpdateTime Uses

func (x *Model) GetUpdateTime() *timestamp.Timestamp

func (*Model) ProtoMessage Uses

func (*Model) ProtoMessage()

func (*Model) ProtoReflect Uses

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

func (*Model) Reset Uses

func (x *Model) Reset()

func (*Model) String Uses

func (x *Model) String() string

type ModelEvaluation Uses

type ModelEvaluation struct {

    // Output only. Problem type specific evaluation metrics.
    //
    // Types that are assignable to Metrics:
    //	*ModelEvaluation_ClassificationEvaluationMetrics
    //	*ModelEvaluation_TranslationEvaluationMetrics
    //	*ModelEvaluation_ImageObjectDetectionEvaluationMetrics
    //	*ModelEvaluation_TextSentimentEvaluationMetrics
    //	*ModelEvaluation_TextExtractionEvaluationMetrics
    Metrics isModelEvaluation_Metrics `protobuf_oneof:"metrics"`
    // Output only. Resource name of the model evaluation.
    // Format:
    //
    // `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
    Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
    // Output only. The ID of the annotation spec that the model evaluation applies to. The
    // The ID is empty for the overall model evaluation.
    // For Tables annotation specs in the dataset do not exist and this ID is
    // always not set, but for CLASSIFICATION
    //
    // [prediction_type-s][google.cloud.automl.v1.TablesModelMetadata.prediction_type]
    // the
    // [display_name][google.cloud.automl.v1.ModelEvaluation.display_name]
    // field is used.
    AnnotationSpecId string `protobuf:"bytes,2,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]
    // at the moment when the model was trained. Because this field returns a
    // value at model training time, for different models trained from the same
    // dataset, the values may differ, since display names could had been changed
    // between the two model's trainings. For Tables CLASSIFICATION
    //
    // [prediction_type-s][google.cloud.automl.v1.TablesModelMetadata.prediction_type]
    // distinct values of the target column at the moment of the model evaluation
    // are populated here.
    // The display_name is empty for the overall model evaluation.
    DisplayName string `protobuf:"bytes,15,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
    // Output only. Timestamp when this model evaluation was created.
    CreateTime *timestamp.Timestamp `protobuf:"bytes,5,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
    // Output only. The number of examples used for model evaluation, i.e. for
    // which ground truth from time of model creation is compared against the
    // predicted annotations created by the model.
    // For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is
    // the total number of all examples used for evaluation.
    // Otherwise, this is the count of examples that according to the ground
    // truth were annotated by the
    //
    // [annotation_spec_id][google.cloud.automl.v1.ModelEvaluation.annotation_spec_id].
    EvaluatedExampleCount int32 `protobuf:"varint,6,opt,name=evaluated_example_count,json=evaluatedExampleCount,proto3" json:"evaluated_example_count,omitempty"`
    // contains filtered or unexported fields
}

Evaluation results of a model.

func (*ModelEvaluation) Descriptor Uses

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

Deprecated: Use ModelEvaluation.ProtoReflect.Descriptor instead.

func (*ModelEvaluation) GetAnnotationSpecId Uses

func (x *ModelEvaluation) GetAnnotationSpecId() string

func (*ModelEvaluation) GetClassificationEvaluationMetrics Uses

func (x *ModelEvaluation) GetClassificationEvaluationMetrics() *ClassificationEvaluationMetrics

func (*ModelEvaluation) GetCreateTime Uses

func (x *ModelEvaluation) GetCreateTime() *timestamp.Timestamp

func (*ModelEvaluation) GetDisplayName Uses

func (x *ModelEvaluation) GetDisplayName() string

func (*ModelEvaluation) GetEvaluatedExampleCount Uses

func (x *ModelEvaluation) GetEvaluatedExampleCount() int32

func (*ModelEvaluation) GetImageObjectDetectionEvaluationMetrics Uses

func (x *ModelEvaluation) GetImageObjectDetectionEvaluationMetrics() *ImageObjectDetectionEvaluationMetrics

func (*ModelEvaluation) GetMetrics Uses

func (m *ModelEvaluation) GetMetrics() isModelEvaluation_Metrics

func (*ModelEvaluation) GetName Uses

func (x *ModelEvaluation) GetName() string

func (*ModelEvaluation) GetTextExtractionEvaluationMetrics Uses

func (x *ModelEvaluation) GetTextExtractionEvaluationMetrics() *TextExtractionEvaluationMetrics

func (*ModelEvaluation) GetTextSentimentEvaluationMetrics Uses

func (x *ModelEvaluation) GetTextSentimentEvaluationMetrics() *TextSentimentEvaluationMetrics

func (*ModelEvaluation) GetTranslationEvaluationMetrics Uses

func (x *ModelEvaluation) GetTranslationEvaluationMetrics() *TranslationEvaluationMetrics

func (*ModelEvaluation) ProtoMessage Uses

func (*ModelEvaluation) ProtoMessage()

func (*ModelEvaluation) ProtoReflect Uses

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

func (*ModelEvaluation) Reset Uses

func (x *ModelEvaluation) Reset()

func (*ModelEvaluation) String Uses

func (x *ModelEvaluation) String() string

type ModelEvaluation_ClassificationEvaluationMetrics Uses

type ModelEvaluation_ClassificationEvaluationMetrics struct {
    // Model evaluation metrics for image, text, video and tables
    // classification.
    // Tables problem is considered a classification when the target column
    // is CATEGORY DataType.
    ClassificationEvaluationMetrics *ClassificationEvaluationMetrics `protobuf:"bytes,8,opt,name=classification_evaluation_metrics,json=classificationEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics Uses

type ModelEvaluation_ImageObjectDetectionEvaluationMetrics struct {
    // Model evaluation metrics for image object detection.
    ImageObjectDetectionEvaluationMetrics *ImageObjectDetectionEvaluationMetrics `protobuf:"bytes,12,opt,name=image_object_detection_evaluation_metrics,json=imageObjectDetectionEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TextExtractionEvaluationMetrics Uses

type ModelEvaluation_TextExtractionEvaluationMetrics struct {
    // Evaluation metrics for text extraction models.
    TextExtractionEvaluationMetrics *TextExtractionEvaluationMetrics `protobuf:"bytes,13,opt,name=text_extraction_evaluation_metrics,json=textExtractionEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TextSentimentEvaluationMetrics Uses

type ModelEvaluation_TextSentimentEvaluationMetrics struct {
    // Evaluation metrics for text sentiment models.
    TextSentimentEvaluationMetrics *TextSentimentEvaluationMetrics `protobuf:"bytes,11,opt,name=text_sentiment_evaluation_metrics,json=textSentimentEvaluationMetrics,proto3,oneof"`
}

type ModelEvaluation_TranslationEvaluationMetrics Uses

type ModelEvaluation_TranslationEvaluationMetrics struct {
    // Model evaluation metrics for translation.
    TranslationEvaluationMetrics *TranslationEvaluationMetrics `protobuf:"bytes,9,opt,name=translation_evaluation_metrics,json=translationEvaluationMetrics,proto3,oneof"`
}

type ModelExportOutputConfig Uses

type ModelExportOutputConfig struct {

    // The destination of the output.
    //
    // Types that are assignable to Destination:
    //	*ModelExportOutputConfig_GcsDestination
    Destination isModelExportOutputConfig_Destination `protobuf_oneof:"destination"`
    // The format in which the model must be exported. The available, and default,
    // formats depend on the problem and model type (if given problem and type
    // combination doesn't have a format listed, it means its models are not
    // exportable):
    //
    // *  For Image Classification mobile-low-latency-1, mobile-versatile-1,
    //        mobile-high-accuracy-1:
    //      "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js",
    //      "docker".
    //
    // *  For Image Classification mobile-core-ml-low-latency-1,
    //        mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
    //      "core_ml" (default).
    //
    // *  For Image Object Detection mobile-low-latency-1, mobile-versatile-1,
    //        mobile-high-accuracy-1:
    //      "tflite", "tf_saved_model", "tf_js".
    // Formats description:
    //
    // * tflite - Used for Android mobile devices.
    // * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/)
    //                    devices.
    // * tf_saved_model - A tensorflow model in SavedModel format.
    // * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can
    //           be used in the browser and in Node.js using JavaScript.
    // * docker - Used for Docker containers. Use the params field to customize
    //            the container. The container is verified to work correctly on
    //            ubuntu 16.04 operating system. See more at
    //            [containers
    //
    // quickstart](https:
    // //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
    // * core_ml - Used for iOS mobile devices.
    ModelFormat string `protobuf:"bytes,4,opt,name=model_format,json=modelFormat,proto3" json:"model_format,omitempty"`
    // Additional model-type and format specific parameters describing the
    // requirements for the to be exported model files, any string must be up to
    // 25000 characters long.
    //
    //  * For `docker` format:
    //     `cpu_architecture` - (string) "x86_64" (default).
    //     `gpu_architecture` - (string) "none" (default), "nvidia".
    Params map[string]string `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

Output configuration for ModelExport Action.

func (*ModelExportOutputConfig) Descriptor Uses

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

Deprecated: Use ModelExportOutputConfig.ProtoReflect.Descriptor instead.

func (*ModelExportOutputConfig) GetDestination Uses

func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destination

func (*ModelExportOutputConfig) GetGcsDestination Uses

func (x *ModelExportOutputConfig) GetGcsDestination() *GcsDestination

func (*ModelExportOutputConfig) GetModelFormat Uses

func (x *ModelExportOutputConfig) GetModelFormat() string

func (*ModelExportOutputConfig) GetParams Uses

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

func (*ModelExportOutputConfig) ProtoMessage Uses

func (*ModelExportOutputConfig) ProtoMessage()

func (*ModelExportOutputConfig) ProtoReflect Uses

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

func (*ModelExportOutputConfig) Reset Uses

func (x *ModelExportOutputConfig) Reset()

func (*ModelExportOutputConfig) String Uses

func (x *ModelExportOutputConfig) String() string

type ModelExportOutputConfig_GcsDestination Uses

type ModelExportOutputConfig_GcsDestination struct {
    // Required. The Google Cloud Storage location where the model is to be written to.
    // This location may only be set for the following model formats:
    //   "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml".
    //
    //  Under the directory given as the destination a new one with name
    //  "model-export-<model-display-name>-<timestamp-of-export-call>",
    //  where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format,
    //  will be created. Inside the model and any of its supporting files
    //  will be written.
    GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

type Model_DeploymentState Uses

type Model_DeploymentState int32

Deployment state of the model.

const (
    // Should not be used, an un-set enum has this value by default.
    Model_DEPLOYMENT_STATE_UNSPECIFIED Model_DeploymentState = 0
    // Model is deployed.
    Model_DEPLOYED Model_DeploymentState = 1
    // Model is not deployed.
    Model_UNDEPLOYED Model_DeploymentState = 2
)

func (Model_DeploymentState) Descriptor Uses

func (Model_DeploymentState) Descriptor() protoreflect.EnumDescriptor

func (Model_DeploymentState) Enum Uses

func (x Model_DeploymentState) Enum() *Model_DeploymentState

func (Model_DeploymentState) EnumDescriptor Uses

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

Deprecated: Use Model_DeploymentState.Descriptor instead.

func (Model_DeploymentState) Number Uses

func (x Model_DeploymentState) Number() protoreflect.EnumNumber

func (Model_DeploymentState) String Uses

func (x Model_DeploymentState) String() string

func (Model_DeploymentState) Type Uses

func (Model_DeploymentState) Type() protoreflect.EnumType

type Model_ImageClassificationModelMetadata Uses

type Model_ImageClassificationModelMetadata struct {
    // Metadata for image classification models.
    ImageClassificationModelMetadata *ImageClassificationModelMetadata `protobuf:"bytes,13,opt,name=image_classification_model_metadata,json=imageClassificationModelMetadata,proto3,oneof"`
}

type Model_ImageObjectDetectionModelMetadata Uses

type Model_ImageObjectDetectionModelMetadata struct {
    // Metadata for image object detection models.
    ImageObjectDetectionModelMetadata *ImageObjectDetectionModelMetadata `protobuf:"bytes,20,opt,name=image_object_detection_model_metadata,json=imageObjectDetectionModelMetadata,proto3,oneof"`
}

type Model_TextClassificationModelMetadata Uses

type Model_TextClassificationModelMetadata struct {
    // Metadata for text classification models.
    TextClassificationModelMetadata *TextClassificationModelMetadata `protobuf:"bytes,14,opt,name=text_classification_model_metadata,json=textClassificationModelMetadata,proto3,oneof"`
}

type Model_TextExtractionModelMetadata Uses

type Model_TextExtractionModelMetadata struct {
    // Metadata for text extraction models.
    TextExtractionModelMetadata *TextExtractionModelMetadata `protobuf:"bytes,19,opt,name=text_extraction_model_metadata,json=textExtractionModelMetadata,proto3,oneof"`
}

type Model_TextSentimentModelMetadata Uses

type Model_TextSentimentModelMetadata struct {
    // Metadata for text sentiment models.
    TextSentimentModelMetadata *TextSentimentModelMetadata `protobuf:"bytes,22,opt,name=text_sentiment_model_metadata,json=textSentimentModelMetadata,proto3,oneof"`
}

type Model_TranslationModelMetadata Uses

type Model_TranslationModelMetadata struct {
    // Metadata for translation models.
    TranslationModelMetadata *TranslationModelMetadata `protobuf:"bytes,15,opt,name=translation_model_metadata,json=translationModelMetadata,proto3,oneof"`
}

type NormalizedVertex Uses

type NormalizedVertex struct {

    // Required. Horizontal coordinate.
    X   float32 `protobuf:"fixed32,1,opt,name=x,proto3" json:"x,omitempty"`
    // Required. Vertical coordinate.
    Y   float32 `protobuf:"fixed32,2,opt,name=y,proto3" json:"y,omitempty"`
    // contains filtered or unexported fields
}

A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.

func (*NormalizedVertex) Descriptor Uses

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

Deprecated: Use NormalizedVertex.ProtoReflect.Descriptor instead.

func (*NormalizedVertex) GetX Uses

func (x *NormalizedVertex) GetX() float32

func (*NormalizedVertex) GetY Uses

func (x *NormalizedVertex) GetY() float32

func (*NormalizedVertex) ProtoMessage Uses

func (*NormalizedVertex) ProtoMessage()

func (*NormalizedVertex) ProtoReflect Uses

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

func (*NormalizedVertex) Reset Uses

func (x *NormalizedVertex) Reset()

func (*NormalizedVertex) String Uses

func (x *NormalizedVertex) String() string

type OperationMetadata Uses

type OperationMetadata struct {

    // Ouptut only. Details of specific operation. Even if this field is empty,
    // the presence allows to distinguish different types of operations.
    //
    // Types that are assignable to Details:
    //	*OperationMetadata_DeleteDetails
    //	*OperationMetadata_DeployModelDetails
    //	*OperationMetadata_UndeployModelDetails
    //	*OperationMetadata_CreateModelDetails
    //	*OperationMetadata_CreateDatasetDetails
    //	*OperationMetadata_ImportDataDetails
    //	*OperationMetadata_BatchPredictDetails
    //	*OperationMetadata_ExportDataDetails
    //	*OperationMetadata_ExportModelDetails
    Details isOperationMetadata_Details `protobuf_oneof:"details"`
    // Output only. Progress of operation. Range: [0, 100].
    // Not used currently.
    ProgressPercent int32 `protobuf:"varint,13,opt,name=progress_percent,json=progressPercent,proto3" json:"progress_percent,omitempty"`
    // Output only. Partial failures encountered.
    // E.g. single files that couldn't be read.
    // This field should never exceed 20 entries.
    // Status details field will contain standard GCP error details.
    PartialFailures []*status.Status `protobuf:"bytes,2,rep,name=partial_failures,json=partialFailures,proto3" json:"partial_failures,omitempty"`
    // Output only. Time when the operation was created.
    CreateTime *timestamp.Timestamp `protobuf:"bytes,3,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
    // Output only. Time when the operation was updated for the last time.
    UpdateTime *timestamp.Timestamp `protobuf:"bytes,4,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
    // contains filtered or unexported fields
}

Metadata used across all long running operations returned by AutoML API.

func (*OperationMetadata) Descriptor Uses

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

Deprecated: Use OperationMetadata.ProtoReflect.Descriptor instead.

func (*OperationMetadata) GetBatchPredictDetails Uses

func (x *OperationMetadata) GetBatchPredictDetails() *BatchPredictOperationMetadata

func (*OperationMetadata) GetCreateDatasetDetails Uses

func (x *OperationMetadata) GetCreateDatasetDetails() *CreateDatasetOperationMetadata

func (*OperationMetadata) GetCreateModelDetails Uses

func (x *OperationMetadata) GetCreateModelDetails() *CreateModelOperationMetadata

func (*OperationMetadata) GetCreateTime Uses

func (x *OperationMetadata) GetCreateTime() *timestamp.Timestamp

func (*OperationMetadata) GetDeleteDetails Uses

func (x *OperationMetadata) GetDeleteDetails() *DeleteOperationMetadata

func (*OperationMetadata) GetDeployModelDetails Uses

func (x *OperationMetadata) GetDeployModelDetails() *DeployModelOperationMetadata

func (*OperationMetadata) GetDetails Uses

func (m *OperationMetadata) GetDetails() isOperationMetadata_Details

func (*OperationMetadata) GetExportDataDetails Uses

func (x *OperationMetadata) GetExportDataDetails() *ExportDataOperationMetadata

func (*OperationMetadata) GetExportModelDetails Uses

func (x *OperationMetadata) GetExportModelDetails() *ExportModelOperationMetadata

func (*OperationMetadata) GetImportDataDetails Uses

func (x *OperationMetadata) GetImportDataDetails() *ImportDataOperationMetadata

func (*OperationMetadata) GetPartialFailures Uses

func (x *OperationMetadata) GetPartialFailures() []*status.Status

func (*OperationMetadata) GetProgressPercent Uses

func (x *OperationMetadata) GetProgressPercent() int32

func (*OperationMetadata) GetUndeployModelDetails Uses

func (x *OperationMetadata) GetUndeployModelDetails() *UndeployModelOperationMetadata

func (*OperationMetadata) GetUpdateTime Uses

func (x *OperationMetadata) GetUpdateTime() *timestamp.Timestamp

func (*OperationMetadata) ProtoMessage Uses

func (*OperationMetadata) ProtoMessage()

func (*OperationMetadata) ProtoReflect Uses

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

func (*OperationMetadata) Reset Uses

func (x *OperationMetadata) Reset()

func (*OperationMetadata) String Uses

func (x *OperationMetadata) String() string

type OperationMetadata_BatchPredictDetails Uses

type OperationMetadata_BatchPredictDetails struct {
    // Details of BatchPredict operation.
    BatchPredictDetails *BatchPredictOperationMetadata `protobuf:"bytes,16,opt,name=batch_predict_details,json=batchPredictDetails,proto3,oneof"`
}

type OperationMetadata_CreateDatasetDetails Uses

type OperationMetadata_CreateDatasetDetails struct {
    // Details of CreateDataset operation.
    CreateDatasetDetails *CreateDatasetOperationMetadata `protobuf:"bytes,30,opt,name=create_dataset_details,json=createDatasetDetails,proto3,oneof"`
}

type OperationMetadata_CreateModelDetails Uses

type OperationMetadata_CreateModelDetails struct {
    // Details of CreateModel operation.
    CreateModelDetails *CreateModelOperationMetadata `protobuf:"bytes,10,opt,name=create_model_details,json=createModelDetails,proto3,oneof"`
}

type OperationMetadata_DeleteDetails Uses

type OperationMetadata_DeleteDetails struct {
    // Details of a Delete operation.
    DeleteDetails *DeleteOperationMetadata `protobuf:"bytes,8,opt,name=delete_details,json=deleteDetails,proto3,oneof"`
}

type OperationMetadata_DeployModelDetails Uses

type OperationMetadata_DeployModelDetails struct {
    // Details of a DeployModel operation.
    DeployModelDetails *DeployModelOperationMetadata `protobuf:"bytes,24,opt,name=deploy_model_details,json=deployModelDetails,proto3,oneof"`
}

type OperationMetadata_ExportDataDetails Uses

type OperationMetadata_ExportDataDetails struct {
    // Details of ExportData operation.
    ExportDataDetails *ExportDataOperationMetadata `protobuf:"bytes,21,opt,name=export_data_details,json=exportDataDetails,proto3,oneof"`
}

type OperationMetadata_ExportModelDetails Uses

type OperationMetadata_ExportModelDetails struct {
    // Details of ExportModel operation.
    ExportModelDetails *ExportModelOperationMetadata `protobuf:"bytes,22,opt,name=export_model_details,json=exportModelDetails,proto3,oneof"`
}

type OperationMetadata_ImportDataDetails Uses

type OperationMetadata_ImportDataDetails struct {
    // Details of ImportData operation.
    ImportDataDetails *ImportDataOperationMetadata `protobuf:"bytes,15,opt,name=import_data_details,json=importDataDetails,proto3,oneof"`
}

type OperationMetadata_UndeployModelDetails Uses

type OperationMetadata_UndeployModelDetails struct {
    // Details of an UndeployModel operation.
    UndeployModelDetails *UndeployModelOperationMetadata `protobuf:"bytes,25,opt,name=undeploy_model_details,json=undeployModelDetails,proto3,oneof"`
}

type OutputConfig Uses

type OutputConfig struct {

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

* For Translation:

      CSV file `translation.csv`, with each line in format:
      ML_USE,GCS_FILE_PATH
      GCS_FILE_PATH leads to a .TSV file which describes examples that have
      given ML_USE, using the following row format per line:
      TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target
      language)

*  For Tables:
      Output depends on whether the dataset was imported from Google Cloud
      Storage or BigQuery.
      Google Cloud Storage case:

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

  must be set. Exported are CSV file(s) `tables_1.csv`,
  `tables_2.csv`,...,`tables_N.csv` with each having as header line
  the table's column names, and all other lines contain values for
  the header columns.
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

`export_data_<automl-dataset-display-name>_<timestamp-of-export-call>`

where <automl-dataset-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 that
dataset a new table called `primary_table` will be created, and
filled with precisely the same data as this obtained on import.

func (*OutputConfig) Descriptor Uses

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

Deprecated: Use OutputConfig.ProtoReflect.Descriptor instead.

func (*OutputConfig) GetDestination Uses

func (m *OutputConfig) GetDestination() isOutputConfig_Destination

func (*OutputConfig) GetGcsDestination Uses

func (x *OutputConfig) GetGcsDestination() *GcsDestination

func (*OutputConfig) ProtoMessage Uses

func (*OutputConfig) ProtoMessage()

func (*OutputConfig) ProtoReflect Uses

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

func (*OutputConfig) Reset Uses

func (x *OutputConfig) Reset()

func (*OutputConfig) String Uses

func (x *OutputConfig) String() string

type OutputConfig_GcsDestination Uses

type OutputConfig_GcsDestination struct {
    // Required. The Google Cloud Storage location where the output is to be written to.
    // For Image Object Detection, Text Extraction, Video Classification and
    // Tables, in the given directory a new directory will be created with name:
    // export_data-<dataset-display-name>-<timestamp-of-export-call> where
    // timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export
    // output will be written into that directory.
    GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

type PredictRequest Uses

type PredictRequest struct {

    // Required. Name of the model requested to serve the prediction.
    Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
    // Required. Payload to perform a prediction on. The payload must match the
    // problem type that the model was trained to solve.
    Payload *ExamplePayload `protobuf:"bytes,2,opt,name=payload,proto3" json:"payload,omitempty"`
    // Additional domain-specific parameters, any string must be up to 25000
    // characters long.
    //
    // 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. The default is 100. The
    //   number of returned bounding boxes might be limited by the server.
    //
    // AutoML Tables
    //
    // `feature_importance`
    // : (boolean) Whether
    //
    // [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
    //   is populated in the returned list of
    //   [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]
    //   objects. The default is false.
    Params map[string]string `protobuf:"bytes,3,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

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

func (*PredictRequest) Descriptor Uses

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

Deprecated: Use PredictRequest.ProtoReflect.Descriptor instead.

func (*PredictRequest) GetName Uses

func (x *PredictRequest) GetName() string

func (*PredictRequest) GetParams Uses

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

func (*PredictRequest) GetPayload Uses

func (x *PredictRequest) GetPayload() *ExamplePayload

func (*PredictRequest) ProtoMessage Uses

func (*PredictRequest) ProtoMessage()

func (*PredictRequest) ProtoReflect Uses

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

func (*PredictRequest) Reset Uses

func (x *PredictRequest) Reset()

func (*PredictRequest) String Uses

func (x *PredictRequest) String() string

type PredictResponse Uses

type PredictResponse struct {

    // Prediction result.
    // AutoML Translation and AutoML Natural Language Sentiment Analysis
    // return precisely one payload.
    Payload []*AnnotationPayload `protobuf:"bytes,1,rep,name=payload,proto3" json:"payload,omitempty"`
    // The preprocessed example that AutoML actually makes prediction on.
    // Empty if AutoML does not preprocess the input example.
    //
    // For AutoML Natural Language (Classification, Entity Extraction, and
    // Sentiment Analysis), if the input is a document, the recognized text is
    // returned in the
    // [document_text][google.cloud.automl.v1.Document.document_text]
    // property.
    PreprocessedInput *ExamplePayload `protobuf:"bytes,3,opt,name=preprocessed_input,json=preprocessedInput,proto3" json:"preprocessed_input,omitempty"`
    // Additional domain-specific prediction response metadata.
    //
    // AutoML Vision Object Detection
    //
    // `max_bounding_box_count`
    // : (int64) The maximum number of bounding boxes to return per image.
    //
    // AutoML Natural Language Sentiment Analysis
    //
    // `sentiment_score`
    // : (float, deprecated) A value between -1 and 1,
    //   -1 maps to least positive sentiment, while 1 maps to the most positive
    //   one and the higher the score, the more positive the sentiment in the
    //   document is. Yet these values are relative to the training data, so
    //   e.g. if all data was positive then -1 is also positive (though
    //   the least).
    //   `sentiment_score` is not the same as "score" and "magnitude"
    //   from Sentiment Analysis in the Natural Language API.
    Metadata map[string]string `protobuf:"bytes,2,rep,name=metadata,proto3" json:"metadata,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
    // contains filtered or unexported fields
}

Response message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].

func (*PredictResponse) Descriptor Uses

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

Deprecated: Use PredictResponse.ProtoReflect.Descriptor instead.

func (*PredictResponse) GetMetadata Uses

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

func (*PredictResponse) GetPayload Uses

func (x *PredictResponse) GetPayload() []*AnnotationPayload

func (*PredictResponse) GetPreprocessedInput Uses

func (x *PredictResponse) GetPreprocessedInput() *ExamplePayload

func (*PredictResponse) ProtoMessage Uses

func (*PredictResponse) ProtoMessage()

func (*PredictResponse) ProtoReflect Uses

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

func (*PredictResponse) Reset Uses

func (x *PredictResponse) Reset()

func (*PredictResponse) String Uses

func (x *PredictResponse) String() string

type PredictionServiceClient Uses

type PredictionServiceClient interface {
    // Perform an online prediction. The prediction result is directly
    // returned in the response.
    // Available for following ML scenarios, and their expected request payloads:
    //
    // AutoML Vision Classification
    //
    // * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
    //
    // AutoML Vision Object Detection
    //
    // * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
    //
    // AutoML Natural Language Classification
    //
    // * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
    // .PDF, .TIF or .TIFF format with size upto 2MB.
    //
    // AutoML Natural Language Entity Extraction
    //
    // * A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document
    //  in .PDF, .TIF or .TIFF format with size upto 20MB.
    //
    // AutoML Natural Language Sentiment Analysis
    //
    // * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
    // .PDF, .TIF or .TIFF format with size upto 2MB.
    //
    // AutoML Translation
    //
    // * A TextSnippet up to 25,000 characters, UTF-8 encoded.
    //
    // AutoML Tables
    //
    // * A row with column values matching
    //   the columns of the model, up to 5MB. Not available for FORECASTING
    //   `prediction_type`.
    Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*PredictResponse, error)
    // Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1.PredictionService.Predict], batch
    // prediction result won't be immediately available in the response. Instead,
    // a long running operation object is returned. User can poll the operation
    // result via [GetOperation][google.longrunning.Operations.GetOperation]
    // method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult] is returned in
    // the [response][google.longrunning.Operation.response] field.
    // Available for following ML scenarios:
    //
    // * AutoML Vision Classification
    // * AutoML Vision Object Detection
    // * AutoML Video Intelligence Classification
    // * AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
    // * AutoML Natural Language Entity Extraction
    // * AutoML Natural Language Sentiment Analysis
    // * AutoML Tables
    BatchPredict(ctx context.Context, in *BatchPredictRequest, opts ...grpc.CallOption) (*longrunning.Operation, error)
}

PredictionServiceClient is the client API for PredictionService service.

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

func NewPredictionServiceClient Uses

func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient

type PredictionServiceServer Uses

type PredictionServiceServer interface {
    // Perform an online prediction. The prediction result is directly
    // returned in the response.
    // Available for following ML scenarios, and their expected request payloads:
    //
    // AutoML Vision Classification
    //
    // * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
    //
    // AutoML Vision Object Detection
    //
    // * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
    //
    // AutoML Natural Language Classification
    //
    // * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
    // .PDF, .TIF or .TIFF format with size upto 2MB.
    //
    // AutoML Natural Language Entity Extraction
    //
    // * A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document
    //  in .PDF, .TIF or .TIFF format with size upto 20MB.
    //
    // AutoML Natural Language Sentiment Analysis
    //
    // * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
    // .PDF, .TIF or .TIFF format with size upto 2MB.
    //
    // AutoML Translation
    //
    // * A TextSnippet up to 25,000 characters, UTF-8 encoded.
    //
    // AutoML Tables
    //
    // * A row with column values matching
    //   the columns of the model, up to 5MB. Not available for FORECASTING
    //   `prediction_type`.
    Predict(context.Context, *PredictRequest) (*PredictResponse, error)
    // Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1.PredictionService.Predict], batch
    // prediction result won't be immediately available in the response. Instead,
    // a long running operation object is returned. User can poll the operation
    // result via [GetOperation][google.longrunning.Operations.GetOperation]
    // method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult] is returned in
    // the [response][google.longrunning.Operation.response] field.
    // Available for following ML scenarios:
    //
    // * AutoML Vision Classification
    // * AutoML Vision Object Detection
    // * AutoML Video Intelligence Classification
    // * AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
    // * AutoML Natural Language Entity Extraction
    // * AutoML Natural Language Sentiment Analysis
    // * AutoML Tables
    BatchPredict(context.Context, *BatchPredictRequest) (*longrunning.Operation, error)
}

PredictionServiceServer is the server API for PredictionService service.

type TextClassificationDatasetMetadata Uses

type TextClassificationDatasetMetadata struct {

    // Required. Type of the classification problem.
    ClassificationType ClassificationType `protobuf:"varint,1,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1.ClassificationType" json:"classification_type,omitempty"`
    // contains filtered or unexported fields
}

Dataset metadata for classification.

func (*TextClassificationDatasetMetadata) Descriptor Uses

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

Deprecated: Use TextClassificationDatasetMetadata.ProtoReflect.Descriptor instead.

func (*TextClassificationDatasetMetadata) GetClassificationType Uses

func (x *TextClassificationDatasetMetadata) GetClassificationType() ClassificationType

func (*TextClassificationDatasetMetadata) ProtoMessage Uses

func (*TextClassificationDatasetMetadata) ProtoMessage()

func (*TextClassificationDatasetMetadata) ProtoReflect Uses

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

func (*TextClassificationDatasetMetadata) Reset Uses

func (x *TextClassificationDatasetMetadata) Reset()

func (*TextClassificationDatasetMetadata) String Uses

func (x *TextClassificationDatasetMetadata) String() string

type TextClassificationModelMetadata Uses

type TextClassificationModelMetadata struct {

    // Output only. Classification type of the dataset used to train this model.
    ClassificationType ClassificationType `protobuf:"varint,3,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1.ClassificationType" json:"classification_type,omitempty"`
    // contains filtered or unexported fields
}

Model metadata that is specific to text classification.

func (*TextClassificationModelMetadata) Descriptor Uses

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

Deprecated: Use TextClassificationModelMetadata.ProtoReflect.Descriptor instead.

func (*TextClassificationModelMetadata) GetClassificationType Uses

func (x *TextClassificationModelMetadata) GetClassificationType() ClassificationType

func (*TextClassificationModelMetadata) ProtoMessage Uses

func (*TextClassificationModelMetadata) ProtoMessage()

func (*TextClassificationModelMetadata) ProtoReflect Uses

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

func (*TextClassificationModelMetadata) Reset Uses

func (x *TextClassificationModelMetadata) Reset()

func (*TextClassificationModelMetadata) String Uses

func (x *TextClassificationModelMetadata) String() string

type TextExtractionAnnotation Uses

type TextExtractionAnnotation struct {

    // Required. Text extraction annotations can either be a text segment or a
    // text relation.
    //
    // Types that are assignable to Annotation:
    //	*TextExtractionAnnotation_TextSegment
    Annotation isTextExtractionAnnotation_Annotation `protobuf_oneof:"annotation"`
    // Output only. A confidence estimate between 0.0 and 1.0. A higher value
    // means greater confidence in correctness of the annotation.
    Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"`
    // contains filtered or unexported fields
}

Annotation for identifying spans of text.

func (*TextExtractionAnnotation) Descriptor Uses

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

Deprecated: Use TextExtractionAnnotation.ProtoReflect.Descriptor instead.

func (*TextExtractionAnnotation) GetAnnotation Uses

func (m *TextExtractionAnnotation) GetAnnotation() isTextExtractionAnnotation_Annotation

func (*TextExtractionAnnotation) GetScore Uses

func (x *TextExtractionAnnotation) GetScore() float32

func (*TextExtractionAnnotation) GetTextSegment Uses

func (x *TextExtractionAnnotation) GetTextSegment() *TextSegment

func (*TextExtractionAnnotation) ProtoMessage Uses

func (*TextExtractionAnnotation) ProtoMessage()

func (*TextExtractionAnnotation) ProtoReflect Uses

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

func (*TextExtractionAnnotation) Reset Uses

func (x *TextExtractionAnnotation) Reset()

func (*TextExtractionAnnotation) String Uses

func (x *TextExtractionAnnotation) String() string

type TextExtractionAnnotation_TextSegment Uses

type TextExtractionAnnotation_TextSegment struct {
    // An entity annotation will set this, which is the part of the original
    // text to which the annotation pertains.
    TextSegment *TextSegment `protobuf:"bytes,3,opt,name=text_segment,json=textSegment,proto3,oneof"`
}

type TextExtractionDatasetMetadata Uses

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

Dataset metadata that is specific to text extraction

func (*TextExtractionDatasetMetadata) Descriptor Uses

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

Deprecated: Use TextExtractionDatasetMetadata.ProtoReflect.Descriptor instead.

func (*TextExtractionDatasetMetadata) ProtoMessage Uses

func (*TextExtractionDatasetMetadata) ProtoMessage()

func (*TextExtractionDatasetMetadata) ProtoReflect Uses

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

func (*TextExtractionDatasetMetadata) Reset Uses

func (x *TextExtractionDatasetMetadata) Reset()

func (*TextExtractionDatasetMetadata) String Uses

func (x *TextExtractionDatasetMetadata) String() string

type TextExtractionEvaluationMetrics Uses

type TextExtractionEvaluationMetrics struct {

    // Output only. The Area under precision recall curve metric.
    AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"`
    // Output only. Metrics that have confidence thresholds.
    // Precision-recall curve can be derived from it.
    ConfidenceMetricsEntries []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry `protobuf:"bytes,2,rep,name=confidence_metrics_entries,json=confidenceMetricsEntries,proto3" json:"confidence_metrics_entries,omitempty"`
    // contains filtered or unexported fields
}

Model evaluation metrics for text extraction problems.

func (*TextExtractionEvaluationMetrics) Descriptor Uses

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

Deprecated: Use TextExtractionEvaluationMetrics.ProtoReflect.Descriptor instead.

func (*TextExtractionEvaluationMetrics) GetAuPrc Uses

func (x *TextExtractionEvaluationMetrics) GetAuPrc() float32

func (*TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries Uses

func (x *TextExtractionEvaluationMetrics) GetConfidenceMetricsEntries() []*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry

func (*TextExtractionEvaluationMetrics) ProtoMessage Uses

func (*TextExtractionEvaluationMetrics) ProtoMessage()

func (*TextExtractionEvaluationMetrics) ProtoReflect Uses

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

func (*TextExtractionEvaluationMetrics) Reset Uses

func (x *TextExtractionEvaluationMetrics) Reset()

func (*TextExtractionEvaluationMetrics) String Uses

func (x *TextExtractionEvaluationMetrics) String() string

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry Uses

type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry struct {

    // Output only. The confidence threshold value used to compute the metrics.
    // Only annotations with score of at least this threshold are considered to
    // be ones the model would return.
    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:"fixed32,3,opt,name=recall,proto3" json:"recall,omitempty"`
    // Output only. Precision under the given confidence threshold.
    Precision float32 `protobuf:"fixed32,4,opt,name=precision,proto3" json:"precision,omitempty"`
    // Output only. The harmonic mean of recall and precision.
    F1Score float32 `protobuf:"fixed32,5,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"`
    // contains filtered or unexported fields
}

Metrics for a single confidence threshold.

func (*TextExtractionEvaluationMetrics_ConfidenceMetricsEntry) Descriptor Uses

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

Deprecated: Use TextExtractionEvaluationMetrics_ConfidenceMetricsEntry.ProtoReflect.Descriptor instead.