// Copyright 2022 Google LLC // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // Code generated by aliasgen. DO NOT EDIT. // Package automl aliases all exported identifiers in package // "cloud.google.com/go/automl/apiv1beta1/automlpb". // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb. // Please read https://github.com/googleapis/google-cloud-go/blob/main/migration.md // for more details. package automl import ( src "cloud.google.com/go/automl/apiv1beta1/automlpb" grpc "google.golang.org/grpc" ) // Deprecated: Please use consts in: cloud.google.com/go/automl/apiv1beta1/automlpb const ( ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED = src.ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType_MULTICLASS = src.ClassificationType_MULTICLASS ClassificationType_MULTILABEL = src.ClassificationType_MULTILABEL DocumentDimensions_CENTIMETER = src.DocumentDimensions_CENTIMETER DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED = src.DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED DocumentDimensions_INCH = src.DocumentDimensions_INCH DocumentDimensions_POINT = src.DocumentDimensions_POINT Document_Layout_FORM_FIELD = src.Document_Layout_FORM_FIELD Document_Layout_FORM_FIELD_CONTENTS = src.Document_Layout_FORM_FIELD_CONTENTS Document_Layout_FORM_FIELD_NAME = src.Document_Layout_FORM_FIELD_NAME Document_Layout_PARAGRAPH = src.Document_Layout_PARAGRAPH Document_Layout_TABLE = src.Document_Layout_TABLE Document_Layout_TABLE_CELL = src.Document_Layout_TABLE_CELL Document_Layout_TABLE_HEADER = src.Document_Layout_TABLE_HEADER Document_Layout_TABLE_ROW = src.Document_Layout_TABLE_ROW Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED = src.Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED Document_Layout_TOKEN = src.Document_Layout_TOKEN Model_DEPLOYED = src.Model_DEPLOYED Model_DEPLOYMENT_STATE_UNSPECIFIED = src.Model_DEPLOYMENT_STATE_UNSPECIFIED Model_UNDEPLOYED = src.Model_UNDEPLOYED TypeCode_ARRAY = src.TypeCode_ARRAY TypeCode_CATEGORY = src.TypeCode_CATEGORY TypeCode_FLOAT64 = src.TypeCode_FLOAT64 TypeCode_STRING = src.TypeCode_STRING TypeCode_STRUCT = src.TypeCode_STRUCT TypeCode_TIMESTAMP = src.TypeCode_TIMESTAMP TypeCode_TYPE_CODE_UNSPECIFIED = src.TypeCode_TYPE_CODE_UNSPECIFIED ) // Deprecated: Please use vars in: cloud.google.com/go/automl/apiv1beta1/automlpb var ( ClassificationType_name = src.ClassificationType_name ClassificationType_value = src.ClassificationType_value DocumentDimensions_DocumentDimensionUnit_name = src.DocumentDimensions_DocumentDimensionUnit_name DocumentDimensions_DocumentDimensionUnit_value = src.DocumentDimensions_DocumentDimensionUnit_value Document_Layout_TextSegmentType_name = src.Document_Layout_TextSegmentType_name Document_Layout_TextSegmentType_value = src.Document_Layout_TextSegmentType_value File_google_cloud_automl_v1beta1_annotation_payload_proto = src.File_google_cloud_automl_v1beta1_annotation_payload_proto File_google_cloud_automl_v1beta1_annotation_spec_proto = src.File_google_cloud_automl_v1beta1_annotation_spec_proto File_google_cloud_automl_v1beta1_classification_proto = src.File_google_cloud_automl_v1beta1_classification_proto File_google_cloud_automl_v1beta1_column_spec_proto = src.File_google_cloud_automl_v1beta1_column_spec_proto File_google_cloud_automl_v1beta1_data_items_proto = src.File_google_cloud_automl_v1beta1_data_items_proto File_google_cloud_automl_v1beta1_data_stats_proto = src.File_google_cloud_automl_v1beta1_data_stats_proto File_google_cloud_automl_v1beta1_data_types_proto = src.File_google_cloud_automl_v1beta1_data_types_proto File_google_cloud_automl_v1beta1_dataset_proto = src.File_google_cloud_automl_v1beta1_dataset_proto File_google_cloud_automl_v1beta1_detection_proto = src.File_google_cloud_automl_v1beta1_detection_proto File_google_cloud_automl_v1beta1_geometry_proto = src.File_google_cloud_automl_v1beta1_geometry_proto File_google_cloud_automl_v1beta1_image_proto = src.File_google_cloud_automl_v1beta1_image_proto File_google_cloud_automl_v1beta1_io_proto = src.File_google_cloud_automl_v1beta1_io_proto File_google_cloud_automl_v1beta1_model_evaluation_proto = src.File_google_cloud_automl_v1beta1_model_evaluation_proto File_google_cloud_automl_v1beta1_model_proto = src.File_google_cloud_automl_v1beta1_model_proto File_google_cloud_automl_v1beta1_operations_proto = src.File_google_cloud_automl_v1beta1_operations_proto File_google_cloud_automl_v1beta1_prediction_service_proto = src.File_google_cloud_automl_v1beta1_prediction_service_proto File_google_cloud_automl_v1beta1_ranges_proto = src.File_google_cloud_automl_v1beta1_ranges_proto File_google_cloud_automl_v1beta1_regression_proto = src.File_google_cloud_automl_v1beta1_regression_proto File_google_cloud_automl_v1beta1_service_proto = src.File_google_cloud_automl_v1beta1_service_proto File_google_cloud_automl_v1beta1_table_spec_proto = src.File_google_cloud_automl_v1beta1_table_spec_proto File_google_cloud_automl_v1beta1_tables_proto = src.File_google_cloud_automl_v1beta1_tables_proto File_google_cloud_automl_v1beta1_temporal_proto = src.File_google_cloud_automl_v1beta1_temporal_proto File_google_cloud_automl_v1beta1_text_extraction_proto = src.File_google_cloud_automl_v1beta1_text_extraction_proto File_google_cloud_automl_v1beta1_text_proto = src.File_google_cloud_automl_v1beta1_text_proto File_google_cloud_automl_v1beta1_text_segment_proto = src.File_google_cloud_automl_v1beta1_text_segment_proto File_google_cloud_automl_v1beta1_text_sentiment_proto = src.File_google_cloud_automl_v1beta1_text_sentiment_proto File_google_cloud_automl_v1beta1_translation_proto = src.File_google_cloud_automl_v1beta1_translation_proto File_google_cloud_automl_v1beta1_video_proto = src.File_google_cloud_automl_v1beta1_video_proto Model_DeploymentState_name = src.Model_DeploymentState_name Model_DeploymentState_value = src.Model_DeploymentState_value TypeCode_name = src.TypeCode_name TypeCode_value = src.TypeCode_value ) // Contains annotation information that is relevant to AutoML. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type AnnotationPayload = src.AnnotationPayload type AnnotationPayload_Classification = src.AnnotationPayload_Classification type AnnotationPayload_ImageObjectDetection = src.AnnotationPayload_ImageObjectDetection type AnnotationPayload_Tables = src.AnnotationPayload_Tables type AnnotationPayload_TextExtraction = src.AnnotationPayload_TextExtraction type AnnotationPayload_TextSentiment = src.AnnotationPayload_TextSentiment type AnnotationPayload_Translation = src.AnnotationPayload_Translation type AnnotationPayload_VideoClassification = src.AnnotationPayload_VideoClassification type AnnotationPayload_VideoObjectTracking = src.AnnotationPayload_VideoObjectTracking // A definition of an annotation spec. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type AnnotationSpec = src.AnnotationSpec // The data statistics of a series of ARRAY values. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ArrayStats = src.ArrayStats // 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type AutoMlClient = src.AutoMlClient // AutoMlServer is the server API for AutoMl service. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type AutoMlServer = src.AutoMlServer // 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.v1beta1.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: - For Image Classification: CSV file(s) // with each line having just a single column: GCS_FILE_PATH which leads to // image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This // path is treated as the ID in the Batch predict output. Three sample rows: // gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png - For // Image Object Detection: CSV file(s) with each line having just a single // column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported // extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch // predict output. Three sample rows: gs://folder/image1.jpeg // gs://folder/image2.gif gs://folder/image3.png - For Video Classification: // CSV file(s) with each line in format: // GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to // 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 end has to be after the start. Three // sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 // gs://folder/vid2.mov,0,inf - For Video Object Tracking: CSV file(s) with // each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END // GCS_FILE_PATH leads to 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 end has to be // after the start. Three sample rows: gs://folder/video1.mp4,10,240 // gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf - For Text // Classification: CSV file(s) with each line having just a single column: // GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. // Any given text snippet content must have 60,000 characters or less. Three // sample rows: gs://folder/text1.txt "Some text content to predict" // gs://folder/text3.pdf Supported file extensions: .txt, .pdf - For Text // Sentiment: CSV file(s) with each line having just a single column: // GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. // Any given text snippet content must have 500 characters or less. Three // sample rows: gs://folder/text1.txt "Some text content to predict" // gs://folder/text3.pdf Supported file extensions: .txt, .pdf - For Text // Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text // in-line or as documents (for a single BatchPredict call only one of the // these formats may be used). The in-line .JSONL file(s) contain 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. The document .JSONL file(s) contain, // per line, a proto that wraps a Document proto with input_config set. Only // PDF documents are supported now, and each document must be up to 2MB large. // Any given .JSONL file must be 100MB or smaller, and no more than 20 files // may be given. Sample in-line JSON Lines file (presented here with artificial // line breaks, but the only actual line break is 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": "An elaborate content", // "mime_type": "text/plain" } } Sample document JSON Lines file (presented // here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } } - For // Tables: Either // [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or // [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]. // GCS case: 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.v1beta1.TablesModelMetadata.input_feature_column_specs] // [display_name-s][google.cloud.automl.v1beta1.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. // For FORECASTING // [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: // all columns having // [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] // type will be ignored. First three sample rows of a CSV file: "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"}]} // BigQuery case: An 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.v1beta1.TablesModelMetadata.input_feature_column_specs] // [display_name-s][google.cloud.automl.v1beta1.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. For // FORECASTING // [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: // all columns having // [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] // type will be ignored. Definitions: GCS_FILE_PATH = A path to file on GCS, // e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, // UTF-8 encoded, enclosed within double quotes ("") 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 an 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BatchPredictInputConfig = src.BatchPredictInputConfig type BatchPredictInputConfig_BigquerySource = src.BatchPredictInputConfig_BigquerySource type BatchPredictInputConfig_GcsSource = src.BatchPredictInputConfig_GcsSource // Details of BatchPredict operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BatchPredictOperationMetadata = src.BatchPredictOperationMetadata // Further describes this batch predict's output. Supplements // [BatchPredictOutputConfig][google.cloud.automl.v1beta1.BatchPredictOutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BatchPredictOperationMetadata_BatchPredictOutputInfo = src.BatchPredictOperationMetadata_BatchPredictOutputInfo type BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset = src.BatchPredictOperationMetadata_BatchPredictOutputInfo_BigqueryOutputDataset type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory = src.BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory // Output configuration for BatchPredict Action. # As destination the // [gcs_destination][google.cloud.automl.v1beta1.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--", 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" : "" 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" : "" 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" // : "" 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" : "" 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 snippet or input text file // and a list of zero or more AnnotationPayload protos (called annotations), // which have classification detail populated. A single text snippet or file // will be listed only once with all its annotations, and its annotations will // never be split across files. If prediction for any text snippet or file // 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 text snippet or input text 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 snippet or input text file and a list of zero or more // AnnotationPayload protos (called annotations), which have text_sentiment // detail populated. A single text snippet or file will be listed only once // with all its annotations, and its annotations will never be split across // files. If prediction for any text snippet or file 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 text snippet or input text 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" : "" (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.v1beta1.BatchPredictOutputConfig.gcs_destination] // or // [bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination] // is set (either is allowed). GCS 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.v1beta1.TablesModelMetadata.prediction_type]: // Each .csv file will contain a header, listing all columns' // [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] given // on input followed by M target column names in the format of // "<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>__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.v1beta1.TablesAnnotation.score]. For REGRESSION // and FORECASTING // [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: // Each .csv file will contain a header, listing all columns' // [display_name-s][google.cloud.automl.v1beta1.display_name] given on input // followed by the predicted target column with name in the format of // "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1beta1.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.v1beta1.OutputConfig.bigquery_destination] // pointing to a BigQuery project must be set. In the given project a new // dataset will be created with name // `prediction__` where // 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.v1beta1.ColumnSpec.display_name] // followed by the target column with name in the format of // "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1beta1.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.v1beta1.AnnotationPayload], // represented as STRUCT-s, containing // [TablesAnnotation][google.cloud.automl.v1beta1.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.v1beta1.TablesModelMetadata.target_column_spec] // [display_name][google.cloud.automl.v1beta1.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`. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BatchPredictOutputConfig = src.BatchPredictOutputConfig type BatchPredictOutputConfig_BigqueryDestination = src.BatchPredictOutputConfig_BigqueryDestination type BatchPredictOutputConfig_GcsDestination = src.BatchPredictOutputConfig_GcsDestination // Request message for // [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BatchPredictRequest = src.BatchPredictRequest // 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.v1beta1.PredictionService.BatchPredict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BatchPredictResult = src.BatchPredictResult // The BigQuery location for the output content. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BigQueryDestination = src.BigQueryDestination // The BigQuery location for the input content. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BigQuerySource = src.BigQuerySource // Bounding box matching model metrics for a single intersection-over-union // threshold and multiple label match confidence thresholds. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BoundingBoxMetricsEntry = src.BoundingBoxMetricsEntry // Metrics for a single confidence threshold. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BoundingBoxMetricsEntry_ConfidenceMetricsEntry = src.BoundingBoxMetricsEntry_ConfidenceMetricsEntry // 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type BoundingPoly = src.BoundingPoly // The data statistics of a series of CATEGORY values. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type CategoryStats = src.CategoryStats // The statistics of a single CATEGORY value. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type CategoryStats_SingleCategoryStats = src.CategoryStats_SingleCategoryStats // Contains annotation details specific to classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ClassificationAnnotation = src.ClassificationAnnotation // Model evaluation metrics for classification problems. Note: For Video // Classification this metrics only describe quality of the Video // Classification predictions of "segment_classification" type. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ClassificationEvaluationMetrics = src.ClassificationEvaluationMetrics // Metrics for a single confidence threshold. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ClassificationEvaluationMetrics_ConfidenceMetricsEntry = src.ClassificationEvaluationMetrics_ConfidenceMetricsEntry // Confusion matrix of the model running the classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ClassificationEvaluationMetrics_ConfusionMatrix = src.ClassificationEvaluationMetrics_ConfusionMatrix // Output only. A row in the confusion matrix. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ClassificationEvaluationMetrics_ConfusionMatrix_Row = src.ClassificationEvaluationMetrics_ConfusionMatrix_Row // Type of the classification problem. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ClassificationType = src.ClassificationType // A representation of a column in a relational table. When listing them, // column specs are returned in the same order in which they were given on // import . Used by: - Tables // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ColumnSpec = src.ColumnSpec // Identifies the table's column, and its correlation with the column this // ColumnSpec describes. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ColumnSpec_CorrelatedColumn = src.ColumnSpec_CorrelatedColumn // A correlation statistics between two series of DataType values. The series // may have differing DataType-s, but within a single series the DataType must // be the same. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type CorrelationStats = src.CorrelationStats // Request message for // [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type CreateDatasetRequest = src.CreateDatasetRequest // Details of CreateModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type CreateModelOperationMetadata = src.CreateModelOperationMetadata // Request message for // [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type CreateModelRequest = src.CreateModelRequest // The data statistics of a series of values that share the same DataType. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DataStats = src.DataStats type DataStats_ArrayStats = src.DataStats_ArrayStats type DataStats_CategoryStats = src.DataStats_CategoryStats type DataStats_Float64Stats = src.DataStats_Float64Stats type DataStats_StringStats = src.DataStats_StringStats type DataStats_StructStats = src.DataStats_StructStats type DataStats_TimestampStats = src.DataStats_TimestampStats // Indicated the type of data that can be stored in a structured data entity // (e.g. a table). // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DataType = src.DataType type DataType_ListElementType = src.DataType_ListElementType type DataType_StructType = src.DataType_StructType type DataType_TimeFormat = src.DataType_TimeFormat // A workspace for solving a single, particular machine learning (ML) problem. // A workspace contains examples that may be annotated. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Dataset = src.Dataset type Dataset_ImageClassificationDatasetMetadata = src.Dataset_ImageClassificationDatasetMetadata type Dataset_ImageObjectDetectionDatasetMetadata = src.Dataset_ImageObjectDetectionDatasetMetadata type Dataset_TablesDatasetMetadata = src.Dataset_TablesDatasetMetadata type Dataset_TextClassificationDatasetMetadata = src.Dataset_TextClassificationDatasetMetadata type Dataset_TextExtractionDatasetMetadata = src.Dataset_TextExtractionDatasetMetadata type Dataset_TextSentimentDatasetMetadata = src.Dataset_TextSentimentDatasetMetadata type Dataset_TranslationDatasetMetadata = src.Dataset_TranslationDatasetMetadata type Dataset_VideoClassificationDatasetMetadata = src.Dataset_VideoClassificationDatasetMetadata type Dataset_VideoObjectTrackingDatasetMetadata = src.Dataset_VideoObjectTrackingDatasetMetadata // Request message for // [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DeleteDatasetRequest = src.DeleteDatasetRequest // Request message for // [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DeleteModelRequest = src.DeleteModelRequest // Details of operations that perform deletes of any entities. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DeleteOperationMetadata = src.DeleteOperationMetadata // Details of DeployModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DeployModelOperationMetadata = src.DeployModelOperationMetadata // Request message for // [AutoMl.DeployModel][google.cloud.automl.v1beta1.AutoMl.DeployModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DeployModelRequest = src.DeployModelRequest type DeployModelRequest_ImageClassificationModelDeploymentMetadata = src.DeployModelRequest_ImageClassificationModelDeploymentMetadata type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata = src.DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata // A structured text document e.g. a PDF. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Document = src.Document // Message that describes dimension of a document. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DocumentDimensions = src.DocumentDimensions // Unit of the document dimension. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DocumentDimensions_DocumentDimensionUnit = src.DocumentDimensions_DocumentDimensionUnit // Input configuration of a [Document][google.cloud.automl.v1beta1.Document]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DocumentInputConfig = src.DocumentInputConfig // Describes the layout information of a // [text_segment][google.cloud.automl.v1beta1.Document.Layout.text_segment] in // the document. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Document_Layout = src.Document_Layout // The type of TextSegment in the context of the original document. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Document_Layout_TextSegmentType = src.Document_Layout_TextSegmentType // A range between two double numbers. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type DoubleRange = src.DoubleRange // Example data used for training or prediction. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExamplePayload = src.ExamplePayload type ExamplePayload_Document = src.ExamplePayload_Document type ExamplePayload_Image = src.ExamplePayload_Image type ExamplePayload_Row = src.ExamplePayload_Row type ExamplePayload_TextSnippet = src.ExamplePayload_TextSnippet // Details of ExportData operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportDataOperationMetadata = src.ExportDataOperationMetadata // Further describes this export data's output. Supplements // [OutputConfig][google.cloud.automl.v1beta1.OutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportDataOperationMetadata_ExportDataOutputInfo = src.ExportDataOperationMetadata_ExportDataOutputInfo type ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset = src.ExportDataOperationMetadata_ExportDataOutputInfo_BigqueryOutputDataset type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory = src.ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory // Request message for // [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportDataRequest = src.ExportDataRequest // Details of EvaluatedExamples operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportEvaluatedExamplesOperationMetadata = src.ExportEvaluatedExamplesOperationMetadata // Further describes the output of the evaluated examples export. Supplements // [ExportEvaluatedExamplesOutputConfig][google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo = src.ExportEvaluatedExamplesOperationMetadata_ExportEvaluatedExamplesOutputInfo // Output configuration for ExportEvaluatedExamples Action. Note that this // call is available only for 30 days since the moment the model was evaluated. // The output depends on the domain, as follows (note that only examples from // the TEST set are exported): - For Tables: // [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination] // pointing to a BigQuery project must be set. In the given project a new // dataset will be created with name // `export_evaluated_examples__` // where 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 an // `evaluated_examples` table will be created. It will have all the same // columns as the // [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id] // of the [dataset][google.cloud.automl.v1beta1.Model.dataset_id] from which // the model was created, as they were at the moment of model's evaluation // (this includes the target column with its ground truth), followed by a // column called "predicted_". That last column will contain the // model's prediction result for each respective row, given as ARRAY of // [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload], // represented as STRUCT-s, containing // [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportEvaluatedExamplesOutputConfig = src.ExportEvaluatedExamplesOutputConfig type ExportEvaluatedExamplesOutputConfig_BigqueryDestination = src.ExportEvaluatedExamplesOutputConfig_BigqueryDestination // Request message for // [AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportEvaluatedExamplesRequest = src.ExportEvaluatedExamplesRequest // Details of ExportModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportModelOperationMetadata = src.ExportModelOperationMetadata // Further describes the output of model export. Supplements // [ModelExportOutputConfig][google.cloud.automl.v1beta1.ModelExportOutputConfig]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportModelOperationMetadata_ExportModelOutputInfo = src.ExportModelOperationMetadata_ExportModelOutputInfo // Request message for // [AutoMl.ExportModel][google.cloud.automl.v1beta1.AutoMl.ExportModel]. Models // need to be enabled for exporting, otherwise an error code will be returned. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ExportModelRequest = src.ExportModelRequest // The data statistics of a series of FLOAT64 values. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Float64Stats = src.Float64Stats // A bucket of a histogram. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Float64Stats_HistogramBucket = src.Float64Stats_HistogramBucket // The GCR location where the image must be pushed to. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GcrDestination = src.GcrDestination // The Google Cloud Storage location where the output is to be written to. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GcsDestination = src.GcsDestination // The Google Cloud Storage location for the input content. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GcsSource = src.GcsSource // Request message for // [AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.AutoMl.GetAnnotationSpec]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GetAnnotationSpecRequest = src.GetAnnotationSpecRequest // Request message for // [AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GetColumnSpecRequest = src.GetColumnSpecRequest // Request message for // [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GetDatasetRequest = src.GetDatasetRequest // Request message for // [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GetModelEvaluationRequest = src.GetModelEvaluationRequest // Request message for // [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GetModelRequest = src.GetModelRequest // Request message for // [AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type GetTableSpecRequest = src.GetTableSpecRequest // A representation of an image. Only images up to 30MB in size are supported. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Image = src.Image // Dataset metadata that is specific to image classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageClassificationDatasetMetadata = src.ImageClassificationDatasetMetadata // Model deployment metadata specific to Image Classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageClassificationModelDeploymentMetadata = src.ImageClassificationModelDeploymentMetadata // Model metadata for image classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageClassificationModelMetadata = src.ImageClassificationModelMetadata // Annotation details for image object detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageObjectDetectionAnnotation = src.ImageObjectDetectionAnnotation // Dataset metadata specific to image object detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageObjectDetectionDatasetMetadata = src.ImageObjectDetectionDatasetMetadata // Model evaluation metrics for image object detection problems. Evaluates // prediction quality of labeled bounding boxes. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageObjectDetectionEvaluationMetrics = src.ImageObjectDetectionEvaluationMetrics // Model deployment metadata specific to Image Object Detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageObjectDetectionModelDeploymentMetadata = src.ImageObjectDetectionModelDeploymentMetadata // Model metadata specific to image object detection. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImageObjectDetectionModelMetadata = src.ImageObjectDetectionModelMetadata type Image_ImageBytes = src.Image_ImageBytes type Image_InputConfig = src.Image_InputConfig // Details of ImportData operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImportDataOperationMetadata = src.ImportDataOperationMetadata // Request message for // [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ImportDataRequest = src.ImportDataRequest // Input configuration for 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.v1beta1.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: - For Image // Classification: CSV file(s) with each line in format: // ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to // 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, // .ICO For 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 - // For Image Object Detection: CSV file(s) with each line in format: // ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to // image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each // image is assumed to be exhaustively labeled. The minimum allowed // BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per // image are allowed (one BOUNDING_BOX is defined per line). If an image has // not yet been labeled, then it should be mentioned just once with no LABEL // and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known // to not contain any bounding boxes, they should be labelled explictly as // "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. 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,,,,,,,,, // TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,, - For Video // Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH // where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should // lead to another .csv file which describes examples that have 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 end has to be // after the start. 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 shuold 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,,, - For Video Object Tracking: CSV file(s) with each // line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not // be used. The GCS_FILE_PATH should lead to another .csv file which describes // examples that have given ML_USE, using one of 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_IDs 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,,,,,,,,,,, - For Text Extraction: CSV file(s) with // each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL // (that is, JSON Lines) file which either imports text in-line or as // documents. Any given .JSONL file must be 100MB or smaller. The in-line // .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in // json representation) followed by one or more AnnotationPayload protos // (called annotations), which have display_name and text_extraction detail // populated. The given text is expected to be annotated exhaustively, for // example, if you look for animals and text contains "dolphin" that is not // labeled, then "dolphin" is assumed to not be an animal. Any given text // snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded // (ASCII already is). The document .JSONL file contains, per line, a proto // that wraps a Document proto. The Document proto must have either // document_text or input_config set. In document_text case, the Document proto // may also contain the spatial information of the document, including layout, // document dimension and page number. In input_config case, only PDF documents // are supported now, and each document may be up to 2MB large. Currently, // annotations on documents cannot be specified at import. Three sample CSV // rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl // TEST,gs://folder/file3.jsonl Sample in-line JSON Lines file for entity // extraction (presented here with artificial line breaks, but the only actual // line break is denoted by \n).: { "document": { "document_text": {"content": // "dog cat"} "layout": [ { "text_segment": { "start_offset": 0, "end_offset": // 3, }, "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, }, { "text_segment": { // "start_offset": 4, "end_offset": 7, }, "page_number": 1, "bounding_poly": { // "normalized_vertices": [ {"x": 0.4, "y": 0.1}, {"x": 0.4, "y": 0.3}, {"x": // 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], // "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } // "page_count": 1, }, "annotations": [ { "display_name": "animal", // "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, // { "display_name": "animal", "text_extraction": {"text_segment": // {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { // "content": "This dog is good." }, "annotations": [ { "display_name": // "animal", "text_extraction": { "text_segment": {"start_offset": 5, // "end_offset": 8} } } ] } Sample document JSON Lines file (presented here // with artificial line breaks, but the only actual line break is 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.pdf" ] } } } } - For // Text Classification: CSV file(s) with each line in format: // ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and // GCS_FILE_PATH are distinguished by a pattern. If the column content is a // valid gcs file path, i.e. prefixed by "gs://", it will be treated as a // GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it // is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead // to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", // and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, // the column content excluding quotes is treated as to be imported text // snippet. In both cases, the text snippet/file size must be within 128kB. // Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They // have bad food and very rude",RudeService,BadFood // TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service // there.",RudeService VALIDATE,"Stomach ache to go.",BadFood - For Text // Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | // GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by // a pattern. If the column content is a valid gcs file path, that is, prefixed // by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a // TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file // with UTF-8 encoding, for example, "gs://folder/content.txt", and the content // in it is extracted as a text snippet. In TEXT_SNIPPET case, the column // content itself is treated as to be imported text snippet. In both cases, the // text snippet must be up to 500 characters long. Sample rows: // TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need // this product so bad",3 TEST,"Thank you for this product.",4 // VALIDATE,gs://folder/content.txt,2 - For Tables: Either // [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or // [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source] // can be used. All inputs is concatenated into a single // [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name] // 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: "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"}]} // 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. 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 = A path to file on GCS, // e.g. "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 an 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 = A content of a // text snippet, UTF-8 encoded, enclosed within double quotes (""). 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 huge. 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type InputConfig = src.InputConfig type InputConfig_BigquerySource = src.InputConfig_BigquerySource type InputConfig_GcsSource = src.InputConfig_GcsSource // Request message for // [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListColumnSpecsRequest = src.ListColumnSpecsRequest // Response message for // [AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListColumnSpecsResponse = src.ListColumnSpecsResponse // Request message for // [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListDatasetsRequest = src.ListDatasetsRequest // Response message for // [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListDatasetsResponse = src.ListDatasetsResponse // Request message for // [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListModelEvaluationsRequest = src.ListModelEvaluationsRequest // Response message for // [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListModelEvaluationsResponse = src.ListModelEvaluationsResponse // Request message for // [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListModelsRequest = src.ListModelsRequest // Response message for // [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListModelsResponse = src.ListModelsResponse // Request message for // [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListTableSpecsRequest = src.ListTableSpecsRequest // Response message for // [AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ListTableSpecsResponse = src.ListTableSpecsResponse // API proto representing a trained machine learning model. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Model = src.Model // Evaluation results of a model. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ModelEvaluation = src.ModelEvaluation type ModelEvaluation_ClassificationEvaluationMetrics = src.ModelEvaluation_ClassificationEvaluationMetrics type ModelEvaluation_ImageObjectDetectionEvaluationMetrics = src.ModelEvaluation_ImageObjectDetectionEvaluationMetrics type ModelEvaluation_RegressionEvaluationMetrics = src.ModelEvaluation_RegressionEvaluationMetrics type ModelEvaluation_TextExtractionEvaluationMetrics = src.ModelEvaluation_TextExtractionEvaluationMetrics type ModelEvaluation_TextSentimentEvaluationMetrics = src.ModelEvaluation_TextSentimentEvaluationMetrics type ModelEvaluation_TranslationEvaluationMetrics = src.ModelEvaluation_TranslationEvaluationMetrics type ModelEvaluation_VideoObjectTrackingEvaluationMetrics = src.ModelEvaluation_VideoObjectTrackingEvaluationMetrics // Output configuration for ModelExport Action. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type ModelExportOutputConfig = src.ModelExportOutputConfig type ModelExportOutputConfig_GcrDestination = src.ModelExportOutputConfig_GcrDestination type ModelExportOutputConfig_GcsDestination = src.ModelExportOutputConfig_GcsDestination // Deployment state of the model. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Model_DeploymentState = src.Model_DeploymentState type Model_ImageClassificationModelMetadata = src.Model_ImageClassificationModelMetadata type Model_ImageObjectDetectionModelMetadata = src.Model_ImageObjectDetectionModelMetadata type Model_TablesModelMetadata = src.Model_TablesModelMetadata type Model_TextClassificationModelMetadata = src.Model_TextClassificationModelMetadata type Model_TextExtractionModelMetadata = src.Model_TextExtractionModelMetadata type Model_TextSentimentModelMetadata = src.Model_TextSentimentModelMetadata type Model_TranslationModelMetadata = src.Model_TranslationModelMetadata type Model_VideoClassificationModelMetadata = src.Model_VideoClassificationModelMetadata type Model_VideoObjectTrackingModelMetadata = src.Model_VideoObjectTrackingModelMetadata // 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type NormalizedVertex = src.NormalizedVertex // Metadata used across all long running operations returned by AutoML API. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type OperationMetadata = src.OperationMetadata type OperationMetadata_BatchPredictDetails = src.OperationMetadata_BatchPredictDetails type OperationMetadata_CreateModelDetails = src.OperationMetadata_CreateModelDetails type OperationMetadata_DeleteDetails = src.OperationMetadata_DeleteDetails type OperationMetadata_DeployModelDetails = src.OperationMetadata_DeployModelDetails type OperationMetadata_ExportDataDetails = src.OperationMetadata_ExportDataDetails type OperationMetadata_ExportEvaluatedExamplesDetails = src.OperationMetadata_ExportEvaluatedExamplesDetails type OperationMetadata_ExportModelDetails = src.OperationMetadata_ExportModelDetails type OperationMetadata_ImportDataDetails = src.OperationMetadata_ImportDataDetails type OperationMetadata_UndeployModelDetails = src.OperationMetadata_UndeployModelDetails // - 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 GCS or // BigQuery. GCS case: // [gcs_destination][google.cloud.automl.v1beta1.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.v1beta1.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__` where // 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type OutputConfig = src.OutputConfig type OutputConfig_BigqueryDestination = src.OutputConfig_BigqueryDestination type OutputConfig_GcsDestination = src.OutputConfig_GcsDestination // Request message for // [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type PredictRequest = src.PredictRequest // Response message for // [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type PredictResponse = src.PredictResponse // 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. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type PredictionServiceClient = src.PredictionServiceClient // PredictionServiceServer is the server API for PredictionService service. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type PredictionServiceServer = src.PredictionServiceServer // Metrics for regression problems. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type RegressionEvaluationMetrics = src.RegressionEvaluationMetrics // A representation of a row in a relational table. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type Row = src.Row // The data statistics of a series of STRING values. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type StringStats = src.StringStats // The statistics of a unigram. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type StringStats_UnigramStats = src.StringStats_UnigramStats // The data statistics of a series of STRUCT values. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type StructStats = src.StructStats // `StructType` defines the DataType-s of a // [STRUCT][google.cloud.automl.v1beta1.TypeCode.STRUCT] type. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type StructType = src.StructType // A specification of a relational table. The table's schema is represented // via its child column specs. It is pre-populated as part of ImportData by // schema inference algorithm, the version of which is a required parameter of // ImportData InputConfig. Note: While working with a table, at times the // schema may be inconsistent with the data in the table (e.g. string in a // FLOAT64 column). The consistency validation is done upon creation of a // model. Used by: - Tables // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TableSpec = src.TableSpec // Contains annotation details specific to Tables. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TablesAnnotation = src.TablesAnnotation // Metadata for a dataset used for AutoML Tables. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TablesDatasetMetadata = src.TablesDatasetMetadata // An information specific to given column and Tables Model, in context of the // Model and the predictions created by it. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TablesModelColumnInfo = src.TablesModelColumnInfo // Model metadata specific to AutoML Tables. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TablesModelMetadata = src.TablesModelMetadata type TablesModelMetadata_OptimizationObjectivePrecisionValue = src.TablesModelMetadata_OptimizationObjectivePrecisionValue type TablesModelMetadata_OptimizationObjectiveRecallValue = src.TablesModelMetadata_OptimizationObjectiveRecallValue // Dataset metadata for classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextClassificationDatasetMetadata = src.TextClassificationDatasetMetadata // Model metadata that is specific to text classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextClassificationModelMetadata = src.TextClassificationModelMetadata // Annotation for identifying spans of text. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextExtractionAnnotation = src.TextExtractionAnnotation type TextExtractionAnnotation_TextSegment = src.TextExtractionAnnotation_TextSegment // Dataset metadata that is specific to text extraction // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextExtractionDatasetMetadata = src.TextExtractionDatasetMetadata // Model evaluation metrics for text extraction problems. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextExtractionEvaluationMetrics = src.TextExtractionEvaluationMetrics // Metrics for a single confidence threshold. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry = src.TextExtractionEvaluationMetrics_ConfidenceMetricsEntry // Model metadata that is specific to text extraction. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextExtractionModelMetadata = src.TextExtractionModelMetadata // A contiguous part of a text (string), assuming it has an UTF-8 NFC // encoding. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextSegment = src.TextSegment // Contains annotation details specific to text sentiment. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextSentimentAnnotation = src.TextSentimentAnnotation // Dataset metadata for text sentiment. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextSentimentDatasetMetadata = src.TextSentimentDatasetMetadata // Model evaluation metrics for text sentiment problems. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextSentimentEvaluationMetrics = src.TextSentimentEvaluationMetrics // Model metadata that is specific to text sentiment. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextSentimentModelMetadata = src.TextSentimentModelMetadata // A representation of a text snippet. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TextSnippet = src.TextSnippet // A time period inside of an example that has a time dimension (e.g. video). // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TimeSegment = src.TimeSegment // The data statistics of a series of TIMESTAMP values. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TimestampStats = src.TimestampStats // Stats split by a defined in context granularity. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TimestampStats_GranularStats = src.TimestampStats_GranularStats // Annotation details specific to translation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TranslationAnnotation = src.TranslationAnnotation // Dataset metadata that is specific to translation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TranslationDatasetMetadata = src.TranslationDatasetMetadata // Evaluation metrics for the dataset. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TranslationEvaluationMetrics = src.TranslationEvaluationMetrics // Model metadata that is specific to translation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TranslationModelMetadata = src.TranslationModelMetadata // `TypeCode` is used as a part of // [DataType][google.cloud.automl.v1beta1.DataType]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type TypeCode = src.TypeCode // Details of UndeployModel operation. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UndeployModelOperationMetadata = src.UndeployModelOperationMetadata // Request message for // [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel]. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UndeployModelRequest = src.UndeployModelRequest // UnimplementedAutoMlServer can be embedded to have forward compatible // implementations. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UnimplementedAutoMlServer = src.UnimplementedAutoMlServer // UnimplementedPredictionServiceServer can be embedded to have forward // compatible implementations. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UnimplementedPredictionServiceServer = src.UnimplementedPredictionServiceServer // Request message for // [AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec] // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UpdateColumnSpecRequest = src.UpdateColumnSpecRequest // Request message for // [AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset] // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UpdateDatasetRequest = src.UpdateDatasetRequest // Request message for // [AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec] // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type UpdateTableSpecRequest = src.UpdateTableSpecRequest // Contains annotation details specific to video classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoClassificationAnnotation = src.VideoClassificationAnnotation // Dataset metadata specific to video classification. All Video Classification // datasets are treated as multi label. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoClassificationDatasetMetadata = src.VideoClassificationDatasetMetadata // Model metadata specific to video classification. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoClassificationModelMetadata = src.VideoClassificationModelMetadata // Annotation details for video object tracking. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoObjectTrackingAnnotation = src.VideoObjectTrackingAnnotation // Dataset metadata specific to video object tracking. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoObjectTrackingDatasetMetadata = src.VideoObjectTrackingDatasetMetadata // Model evaluation metrics for video object tracking problems. Evaluates // prediction quality of both labeled bounding boxes and labeled tracks (i.e. // series of bounding boxes sharing same label and instance ID). // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoObjectTrackingEvaluationMetrics = src.VideoObjectTrackingEvaluationMetrics // Model metadata specific to video object tracking. // // Deprecated: Please use types in: cloud.google.com/go/automl/apiv1beta1/automlpb type VideoObjectTrackingModelMetadata = src.VideoObjectTrackingModelMetadata // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient { return src.NewAutoMlClient(cc) } // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient { return src.NewPredictionServiceClient(cc) } // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer) { src.RegisterAutoMlServer(s, srv) } // Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1beta1/automlpb func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer) { src.RegisterPredictionServiceServer(s, srv) }