| // Copyright 2020 Google LLC |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| |
| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // versions: |
| // protoc-gen-go v1.22.0 |
| // protoc v3.12.2 |
| // source: google/cloud/automl/v1/io.proto |
| |
| package automl |
| |
| import ( |
| reflect "reflect" |
| sync "sync" |
| |
| proto "github.com/golang/protobuf/proto" |
| _ "google.golang.org/genproto/googleapis/api/annotations" |
| protoreflect "google.golang.org/protobuf/reflect/protoreflect" |
| protoimpl "google.golang.org/protobuf/runtime/protoimpl" |
| ) |
| |
| const ( |
| // Verify that this generated code is sufficiently up-to-date. |
| _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) |
| // Verify that runtime/protoimpl is sufficiently up-to-date. |
| _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) |
| ) |
| |
| // This is a compile-time assertion that a sufficiently up-to-date version |
| // of the legacy proto package is being used. |
| const _ = proto.ProtoPackageIsVersion4 |
| |
| // 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. |
| // |
| type InputConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // 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"` |
| } |
| |
| func (x *InputConfig) Reset() { |
| *x = InputConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *InputConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*InputConfig) ProtoMessage() {} |
| |
| func (x *InputConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[0] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use InputConfig.ProtoReflect.Descriptor instead. |
| func (*InputConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (m *InputConfig) GetSource() isInputConfig_Source { |
| if m != nil { |
| return m.Source |
| } |
| return nil |
| } |
| |
| func (x *InputConfig) GetGcsSource() *GcsSource { |
| if x, ok := x.GetSource().(*InputConfig_GcsSource); ok { |
| return x.GcsSource |
| } |
| return nil |
| } |
| |
| func (x *InputConfig) GetParams() map[string]string { |
| if x != nil { |
| return x.Params |
| } |
| return nil |
| } |
| |
| type isInputConfig_Source interface { |
| isInputConfig_Source() |
| } |
| |
| 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"` |
| } |
| |
| func (*InputConfig_GcsSource) isInputConfig_Source() {} |
| |
| // 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. |
| type BatchPredictInputConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The source of the input. |
| // |
| // Types that are assignable to Source: |
| // *BatchPredictInputConfig_GcsSource |
| Source isBatchPredictInputConfig_Source `protobuf_oneof:"source"` |
| } |
| |
| func (x *BatchPredictInputConfig) Reset() { |
| *x = BatchPredictInputConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[1] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *BatchPredictInputConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*BatchPredictInputConfig) ProtoMessage() {} |
| |
| func (x *BatchPredictInputConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[1] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead. |
| func (*BatchPredictInputConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{1} |
| } |
| |
| func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source { |
| if m != nil { |
| return m.Source |
| } |
| return nil |
| } |
| |
| func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource { |
| if x, ok := x.GetSource().(*BatchPredictInputConfig_GcsSource); ok { |
| return x.GcsSource |
| } |
| return nil |
| } |
| |
| type isBatchPredictInputConfig_Source interface { |
| isBatchPredictInputConfig_Source() |
| } |
| |
| 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"` |
| } |
| |
| func (*BatchPredictInputConfig_GcsSource) isBatchPredictInputConfig_Source() {} |
| |
| // Input configuration of a [Document][google.cloud.automl.v1.Document]. |
| type DocumentInputConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // 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"` |
| } |
| |
| func (x *DocumentInputConfig) Reset() { |
| *x = DocumentInputConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[2] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *DocumentInputConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*DocumentInputConfig) ProtoMessage() {} |
| |
| func (x *DocumentInputConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[2] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead. |
| func (*DocumentInputConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{2} |
| } |
| |
| func (x *DocumentInputConfig) GetGcsSource() *GcsSource { |
| if x != nil { |
| return x.GcsSource |
| } |
| return nil |
| } |
| |
| // * 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. |
| type OutputConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The destination of the output. |
| // |
| // Types that are assignable to Destination: |
| // *OutputConfig_GcsDestination |
| Destination isOutputConfig_Destination `protobuf_oneof:"destination"` |
| } |
| |
| func (x *OutputConfig) Reset() { |
| *x = OutputConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[3] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *OutputConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*OutputConfig) ProtoMessage() {} |
| |
| func (x *OutputConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[3] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use OutputConfig.ProtoReflect.Descriptor instead. |
| func (*OutputConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{3} |
| } |
| |
| func (m *OutputConfig) GetDestination() isOutputConfig_Destination { |
| if m != nil { |
| return m.Destination |
| } |
| return nil |
| } |
| |
| func (x *OutputConfig) GetGcsDestination() *GcsDestination { |
| if x, ok := x.GetDestination().(*OutputConfig_GcsDestination); ok { |
| return x.GcsDestination |
| } |
| return nil |
| } |
| |
| type isOutputConfig_Destination interface { |
| isOutputConfig_Destination() |
| } |
| |
| 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"` |
| } |
| |
| func (*OutputConfig_GcsDestination) isOutputConfig_Destination() {} |
| |
| // 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`. |
| type BatchPredictOutputConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // The destination of the output. |
| // |
| // Types that are assignable to Destination: |
| // *BatchPredictOutputConfig_GcsDestination |
| Destination isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"` |
| } |
| |
| func (x *BatchPredictOutputConfig) Reset() { |
| *x = BatchPredictOutputConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[4] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *BatchPredictOutputConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*BatchPredictOutputConfig) ProtoMessage() {} |
| |
| func (x *BatchPredictOutputConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[4] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead. |
| func (*BatchPredictOutputConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{4} |
| } |
| |
| func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination { |
| if m != nil { |
| return m.Destination |
| } |
| return nil |
| } |
| |
| func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination { |
| if x, ok := x.GetDestination().(*BatchPredictOutputConfig_GcsDestination); ok { |
| return x.GcsDestination |
| } |
| return nil |
| } |
| |
| type isBatchPredictOutputConfig_Destination interface { |
| isBatchPredictOutputConfig_Destination() |
| } |
| |
| 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"` |
| } |
| |
| func (*BatchPredictOutputConfig_GcsDestination) isBatchPredictOutputConfig_Destination() {} |
| |
| // Output configuration for ModelExport Action. |
| type ModelExportOutputConfig struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // 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"` |
| } |
| |
| func (x *ModelExportOutputConfig) Reset() { |
| *x = ModelExportOutputConfig{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[5] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *ModelExportOutputConfig) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*ModelExportOutputConfig) ProtoMessage() {} |
| |
| func (x *ModelExportOutputConfig) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[5] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use ModelExportOutputConfig.ProtoReflect.Descriptor instead. |
| func (*ModelExportOutputConfig) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{5} |
| } |
| |
| func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destination { |
| if m != nil { |
| return m.Destination |
| } |
| return nil |
| } |
| |
| func (x *ModelExportOutputConfig) GetGcsDestination() *GcsDestination { |
| if x, ok := x.GetDestination().(*ModelExportOutputConfig_GcsDestination); ok { |
| return x.GcsDestination |
| } |
| return nil |
| } |
| |
| func (x *ModelExportOutputConfig) GetModelFormat() string { |
| if x != nil { |
| return x.ModelFormat |
| } |
| return "" |
| } |
| |
| func (x *ModelExportOutputConfig) GetParams() map[string]string { |
| if x != nil { |
| return x.Params |
| } |
| return nil |
| } |
| |
| type isModelExportOutputConfig_Destination interface { |
| isModelExportOutputConfig_Destination() |
| } |
| |
| 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"` |
| } |
| |
| func (*ModelExportOutputConfig_GcsDestination) isModelExportOutputConfig_Destination() {} |
| |
| // The Google Cloud Storage location for the input content. |
| type GcsSource struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // 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"` |
| } |
| |
| func (x *GcsSource) Reset() { |
| *x = GcsSource{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[6] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *GcsSource) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*GcsSource) ProtoMessage() {} |
| |
| func (x *GcsSource) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[6] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use GcsSource.ProtoReflect.Descriptor instead. |
| func (*GcsSource) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{6} |
| } |
| |
| func (x *GcsSource) GetInputUris() []string { |
| if x != nil { |
| return x.InputUris |
| } |
| return nil |
| } |
| |
| // The Google Cloud Storage location where the output is to be written to. |
| type GcsDestination struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // 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"` |
| } |
| |
| func (x *GcsDestination) Reset() { |
| *x = GcsDestination{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[7] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *GcsDestination) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*GcsDestination) ProtoMessage() {} |
| |
| func (x *GcsDestination) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_automl_v1_io_proto_msgTypes[7] |
| if protoimpl.UnsafeEnabled && x != nil { |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| if ms.LoadMessageInfo() == nil { |
| ms.StoreMessageInfo(mi) |
| } |
| return ms |
| } |
| return mi.MessageOf(x) |
| } |
| |
| // Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead. |
| func (*GcsDestination) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{7} |
| } |
| |
| func (x *GcsDestination) GetOutputUriPrefix() string { |
| if x != nil { |
| return x.OutputUriPrefix |
| } |
| return "" |
| } |
| |
| var File_google_cloud_automl_v1_io_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_automl_v1_io_proto_rawDesc = []byte{ |
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| 0x3a, 0x3a, 0x43, 0x6c, 0x6f, 0x75, 0x64, 0x3a, 0x3a, 0x41, 0x75, 0x74, 0x6f, 0x4d, 0x4c, 0x3a, |
| 0x3a, 0x56, 0x31, 0x62, 0x06, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x33, |
| } |
| |
| var ( |
| file_google_cloud_automl_v1_io_proto_rawDescOnce sync.Once |
| file_google_cloud_automl_v1_io_proto_rawDescData = file_google_cloud_automl_v1_io_proto_rawDesc |
| ) |
| |
| func file_google_cloud_automl_v1_io_proto_rawDescGZIP() []byte { |
| file_google_cloud_automl_v1_io_proto_rawDescOnce.Do(func() { |
| file_google_cloud_automl_v1_io_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_automl_v1_io_proto_rawDescData) |
| }) |
| return file_google_cloud_automl_v1_io_proto_rawDescData |
| } |
| |
| var file_google_cloud_automl_v1_io_proto_msgTypes = make([]protoimpl.MessageInfo, 10) |
| var file_google_cloud_automl_v1_io_proto_goTypes = []interface{}{ |
| (*InputConfig)(nil), // 0: google.cloud.automl.v1.InputConfig |
| (*BatchPredictInputConfig)(nil), // 1: google.cloud.automl.v1.BatchPredictInputConfig |
| (*DocumentInputConfig)(nil), // 2: google.cloud.automl.v1.DocumentInputConfig |
| (*OutputConfig)(nil), // 3: google.cloud.automl.v1.OutputConfig |
| (*BatchPredictOutputConfig)(nil), // 4: google.cloud.automl.v1.BatchPredictOutputConfig |
| (*ModelExportOutputConfig)(nil), // 5: google.cloud.automl.v1.ModelExportOutputConfig |
| (*GcsSource)(nil), // 6: google.cloud.automl.v1.GcsSource |
| (*GcsDestination)(nil), // 7: google.cloud.automl.v1.GcsDestination |
| nil, // 8: google.cloud.automl.v1.InputConfig.ParamsEntry |
| nil, // 9: google.cloud.automl.v1.ModelExportOutputConfig.ParamsEntry |
| } |
| var file_google_cloud_automl_v1_io_proto_depIdxs = []int32{ |
| 6, // 0: google.cloud.automl.v1.InputConfig.gcs_source:type_name -> google.cloud.automl.v1.GcsSource |
| 8, // 1: google.cloud.automl.v1.InputConfig.params:type_name -> google.cloud.automl.v1.InputConfig.ParamsEntry |
| 6, // 2: google.cloud.automl.v1.BatchPredictInputConfig.gcs_source:type_name -> google.cloud.automl.v1.GcsSource |
| 6, // 3: google.cloud.automl.v1.DocumentInputConfig.gcs_source:type_name -> google.cloud.automl.v1.GcsSource |
| 7, // 4: google.cloud.automl.v1.OutputConfig.gcs_destination:type_name -> google.cloud.automl.v1.GcsDestination |
| 7, // 5: google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination:type_name -> google.cloud.automl.v1.GcsDestination |
| 7, // 6: google.cloud.automl.v1.ModelExportOutputConfig.gcs_destination:type_name -> google.cloud.automl.v1.GcsDestination |
| 9, // 7: google.cloud.automl.v1.ModelExportOutputConfig.params:type_name -> google.cloud.automl.v1.ModelExportOutputConfig.ParamsEntry |
| 8, // [8:8] is the sub-list for method output_type |
| 8, // [8:8] is the sub-list for method input_type |
| 8, // [8:8] is the sub-list for extension type_name |
| 8, // [8:8] is the sub-list for extension extendee |
| 0, // [0:8] is the sub-list for field type_name |
| } |
| |
| func init() { file_google_cloud_automl_v1_io_proto_init() } |
| func file_google_cloud_automl_v1_io_proto_init() { |
| if File_google_cloud_automl_v1_io_proto != nil { |
| return |
| } |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_automl_v1_io_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*InputConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*BatchPredictInputConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*DocumentInputConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*OutputConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*BatchPredictOutputConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ModelExportOutputConfig); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*GcsSource); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*GcsDestination); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[0].OneofWrappers = []interface{}{ |
| (*InputConfig_GcsSource)(nil), |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[1].OneofWrappers = []interface{}{ |
| (*BatchPredictInputConfig_GcsSource)(nil), |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[3].OneofWrappers = []interface{}{ |
| (*OutputConfig_GcsDestination)(nil), |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[4].OneofWrappers = []interface{}{ |
| (*BatchPredictOutputConfig_GcsDestination)(nil), |
| } |
| file_google_cloud_automl_v1_io_proto_msgTypes[5].OneofWrappers = []interface{}{ |
| (*ModelExportOutputConfig_GcsDestination)(nil), |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_automl_v1_io_proto_rawDesc, |
| NumEnums: 0, |
| NumMessages: 10, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_cloud_automl_v1_io_proto_goTypes, |
| DependencyIndexes: file_google_cloud_automl_v1_io_proto_depIdxs, |
| MessageInfos: file_google_cloud_automl_v1_io_proto_msgTypes, |
| }.Build() |
| File_google_cloud_automl_v1_io_proto = out.File |
| file_google_cloud_automl_v1_io_proto_rawDesc = nil |
| file_google_cloud_automl_v1_io_proto_goTypes = nil |
| file_google_cloud_automl_v1_io_proto_depIdxs = nil |
| } |
