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// This file is @generated by prost-build.
/// 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.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct NormalizedVertex {
/// Required. Horizontal coordinate.
#[prost(float, tag = "1")]
pub x: f32,
/// Required. Vertical coordinate.
#[prost(float, tag = "2")]
pub y: f32,
}
/// 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.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BoundingPoly {
/// Output only . The bounding polygon normalized vertices.
#[prost(message, repeated, tag = "2")]
pub normalized_vertices: ::prost::alloc::vec::Vec<NormalizedVertex>,
}
/// Annotation details for image object detection.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ImageObjectDetectionAnnotation {
/// Output only. The rectangle representing the object location.
#[prost(message, optional, tag = "1")]
pub bounding_box: ::core::option::Option<BoundingPoly>,
/// Output only. The confidence that this annotation is positive for the parent example,
/// value in \[0, 1\], higher means higher positivity confidence.
#[prost(float, tag = "2")]
pub score: f32,
}
/// Bounding box matching model metrics for a single intersection-over-union
/// threshold and multiple label match confidence thresholds.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BoundingBoxMetricsEntry {
/// Output only. The intersection-over-union threshold value used to compute
/// this metrics entry.
#[prost(float, tag = "1")]
pub iou_threshold: f32,
/// Output only. The mean average precision, most often close to au_prc.
#[prost(float, tag = "2")]
pub mean_average_precision: f32,
/// Output only. Metrics for each label-match confidence_threshold from
/// 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is
/// derived from them.
#[prost(message, repeated, tag = "3")]
pub confidence_metrics_entries: ::prost::alloc::vec::Vec<
bounding_box_metrics_entry::ConfidenceMetricsEntry,
>,
}
/// Nested message and enum types in `BoundingBoxMetricsEntry`.
pub mod bounding_box_metrics_entry {
/// Metrics for a single confidence threshold.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ConfidenceMetricsEntry {
/// Output only. The confidence threshold value used to compute the metrics.
#[prost(float, tag = "1")]
pub confidence_threshold: f32,
/// Output only. Recall under the given confidence threshold.
#[prost(float, tag = "2")]
pub recall: f32,
/// Output only. Precision under the given confidence threshold.
#[prost(float, tag = "3")]
pub precision: f32,
/// Output only. The harmonic mean of recall and precision.
#[prost(float, tag = "4")]
pub f1_score: f32,
}
}
/// Model evaluation metrics for image object detection problems.
/// Evaluates prediction quality of labeled bounding boxes.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ImageObjectDetectionEvaluationMetrics {
/// Output only. The total number of bounding boxes (i.e. summed over all
/// images) the ground truth used to create this evaluation had.
#[prost(int32, tag = "1")]
pub evaluated_bounding_box_count: i32,
/// Output only. The bounding boxes match metrics for each
/// Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
/// and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99
/// pair.
#[prost(message, repeated, tag = "2")]
pub bounding_box_metrics_entries: ::prost::alloc::vec::Vec<BoundingBoxMetricsEntry>,
/// Output only. The single metric for bounding boxes evaluation:
/// the mean_average_precision averaged over all bounding_box_metrics_entries.
#[prost(float, tag = "3")]
pub bounding_box_mean_average_precision: f32,
}
/// 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.
///
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct InputConfig {
/// 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".
#[prost(btree_map = "string, string", tag = "2")]
pub params: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
/// The source of the input.
#[prost(oneof = "input_config::Source", tags = "1")]
pub source: ::core::option::Option<input_config::Source>,
}
/// Nested message and enum types in `InputConfig`.
pub mod input_config {
/// The source of the input.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Source {
/// 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].
#[prost(message, tag = "1")]
GcsSource(super::GcsSource),
}
}
/// 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.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BatchPredictInputConfig {
/// The source of the input.
#[prost(oneof = "batch_predict_input_config::Source", tags = "1")]
pub source: ::core::option::Option<batch_predict_input_config::Source>,
}
/// Nested message and enum types in `BatchPredictInputConfig`.
pub mod batch_predict_input_config {
/// The source of the input.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Source {
/// Required. The Google Cloud Storage location for the input content.
#[prost(message, tag = "1")]
GcsSource(super::GcsSource),
}
}
/// Input configuration of a [Document][google.cloud.automl.v1.Document].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct DocumentInputConfig {
/// The Google Cloud Storage location of the document file. Only a single path
/// should be given.
///
/// Max supported size: 512MB.
///
/// Supported extensions: .PDF.
#[prost(message, optional, tag = "1")]
pub gcs_source: ::core::option::Option<GcsSource>,
}
/// * 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.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct OutputConfig {
/// The destination of the output.
#[prost(oneof = "output_config::Destination", tags = "1")]
pub destination: ::core::option::Option<output_config::Destination>,
}
/// Nested message and enum types in `OutputConfig`.
pub mod output_config {
/// The destination of the output.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Destination {
/// 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.
#[prost(message, tag = "1")]
GcsDestination(super::GcsDestination),
}
}
/// 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`.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BatchPredictOutputConfig {
/// The destination of the output.
#[prost(oneof = "batch_predict_output_config::Destination", tags = "1")]
pub destination: ::core::option::Option<batch_predict_output_config::Destination>,
}
/// Nested message and enum types in `BatchPredictOutputConfig`.
pub mod batch_predict_output_config {
/// The destination of the output.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Destination {
/// Required. The Google Cloud Storage location of the directory where the output is to
/// be written to.
#[prost(message, tag = "1")]
GcsDestination(super::GcsDestination),
}
}
/// Output configuration for ModelExport Action.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ModelExportOutputConfig {
/// 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.
#[prost(string, tag = "4")]
pub model_format: ::prost::alloc::string::String,
/// 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".
#[prost(btree_map = "string, string", tag = "2")]
pub params: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
/// The destination of the output.
#[prost(oneof = "model_export_output_config::Destination", tags = "1")]
pub destination: ::core::option::Option<model_export_output_config::Destination>,
}
/// Nested message and enum types in `ModelExportOutputConfig`.
pub mod model_export_output_config {
/// The destination of the output.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Destination {
/// 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.
#[prost(message, tag = "1")]
GcsDestination(super::GcsDestination),
}
}
/// The Google Cloud Storage location for the input content.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct GcsSource {
/// 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
#[prost(string, repeated, tag = "1")]
pub input_uris: ::prost::alloc::vec::Vec<::prost::alloc::string::String>,
}
/// The Google Cloud Storage location where the output is to be written to.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct GcsDestination {
/// 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.
#[prost(string, tag = "1")]
pub output_uri_prefix: ::prost::alloc::string::String,
}
/// A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TextSegment {
/// Output only. The content of the TextSegment.
#[prost(string, tag = "3")]
pub content: ::prost::alloc::string::String,
/// Required. Zero-based character index of the first character of the text
/// segment (counting characters from the beginning of the text).
#[prost(int64, tag = "1")]
pub start_offset: i64,
/// Required. Zero-based character index of the first character past the end of
/// the text segment (counting character from the beginning of the text).
/// The character at the end_offset is NOT included in the text segment.
#[prost(int64, tag = "2")]
pub end_offset: i64,
}
/// A representation of an image.
/// Only images up to 30MB in size are supported.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct Image {
/// Output only. HTTP URI to the thumbnail image.
#[prost(string, tag = "4")]
pub thumbnail_uri: ::prost::alloc::string::String,
/// Input only. The data representing the image.
/// For Predict calls [image_bytes][google.cloud.automl.v1.Image.image_bytes] must be set .
#[prost(oneof = "image::Data", tags = "1")]
pub data: ::core::option::Option<image::Data>,
}
/// Nested message and enum types in `Image`.
pub mod image {
/// Input only. The data representing the image.
/// For Predict calls [image_bytes][google.cloud.automl.v1.Image.image_bytes] must be set .
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Data {
/// Image content represented as a stream of bytes.
/// Note: As with all `bytes` fields, protobuffers use a pure binary
/// representation, whereas JSON representations use base64.
#[prost(bytes, tag = "1")]
ImageBytes(::prost::bytes::Bytes),
}
}
/// A representation of a text snippet.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TextSnippet {
/// Required. The content of the text snippet as a string. Up to 250000
/// characters long.
#[prost(string, tag = "1")]
pub content: ::prost::alloc::string::String,
/// Optional. The format of [content][google.cloud.automl.v1.TextSnippet.content]. Currently the only two allowed
/// values are "text/html" and "text/plain". If left blank, the format is
/// automatically determined from the type of the uploaded [content][google.cloud.automl.v1.TextSnippet.content].
#[prost(string, tag = "2")]
pub mime_type: ::prost::alloc::string::String,
/// Output only. HTTP URI where you can download the content.
#[prost(string, tag = "4")]
pub content_uri: ::prost::alloc::string::String,
}
/// Message that describes dimension of a document.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct DocumentDimensions {
/// Unit of the dimension.
#[prost(enumeration = "document_dimensions::DocumentDimensionUnit", tag = "1")]
pub unit: i32,
/// Width value of the document, works together with the unit.
#[prost(float, tag = "2")]
pub width: f32,
/// Height value of the document, works together with the unit.
#[prost(float, tag = "3")]
pub height: f32,
}
/// Nested message and enum types in `DocumentDimensions`.
pub mod document_dimensions {
/// Unit of the document dimension.
#[derive(
Clone,
Copy,
Debug,
PartialEq,
Eq,
Hash,
PartialOrd,
Ord,
::prost::Enumeration
)]
#[repr(i32)]
pub enum DocumentDimensionUnit {
/// Should not be used.
Unspecified = 0,
/// Document dimension is measured in inches.
Inch = 1,
/// Document dimension is measured in centimeters.
Centimeter = 2,
/// Document dimension is measured in points. 72 points = 1 inch.
Point = 3,
}
impl DocumentDimensionUnit {
/// String value of the enum field names used in the ProtoBuf definition.
///
/// The values are not transformed in any way and thus are considered stable
/// (if the ProtoBuf definition does not change) and safe for programmatic use.
pub fn as_str_name(&self) -> &'static str {
match self {
DocumentDimensionUnit::Unspecified => {
"DOCUMENT_DIMENSION_UNIT_UNSPECIFIED"
}
DocumentDimensionUnit::Inch => "INCH",
DocumentDimensionUnit::Centimeter => "CENTIMETER",
DocumentDimensionUnit::Point => "POINT",
}
}
/// Creates an enum from field names used in the ProtoBuf definition.
pub fn from_str_name(value: &str) -> ::core::option::Option<Self> {
match value {
"DOCUMENT_DIMENSION_UNIT_UNSPECIFIED" => Some(Self::Unspecified),
"INCH" => Some(Self::Inch),
"CENTIMETER" => Some(Self::Centimeter),
"POINT" => Some(Self::Point),
_ => None,
}
}
}
}
/// A structured text document e.g. a PDF.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct Document {
/// An input config specifying the content of the document.
#[prost(message, optional, tag = "1")]
pub input_config: ::core::option::Option<DocumentInputConfig>,
/// The plain text version of this document.
#[prost(message, optional, tag = "2")]
pub document_text: ::core::option::Option<TextSnippet>,
/// Describes the layout of the document.
/// Sorted by [page_number][].
#[prost(message, repeated, tag = "3")]
pub layout: ::prost::alloc::vec::Vec<document::Layout>,
/// The dimensions of the page in the document.
#[prost(message, optional, tag = "4")]
pub document_dimensions: ::core::option::Option<DocumentDimensions>,
/// Number of pages in the document.
#[prost(int32, tag = "5")]
pub page_count: i32,
}
/// Nested message and enum types in `Document`.
pub mod document {
/// Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct Layout {
/// Text Segment that represents a segment in
/// [document_text][google.cloud.automl.v1p1beta.Document.document_text].
#[prost(message, optional, tag = "1")]
pub text_segment: ::core::option::Option<super::TextSegment>,
/// Page number of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the original document, starts
/// from 1.
#[prost(int32, tag = "2")]
pub page_number: i32,
/// The position of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the page.
/// Contains exactly 4
/// [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices]
/// and they are connected by edges in the order provided, which will
/// represent a rectangle parallel to the frame. The
/// [NormalizedVertex-s][google.cloud.automl.v1p1beta.NormalizedVertex] are
/// relative to the page.
/// Coordinates are based on top-left as point (0,0).
#[prost(message, optional, tag = "3")]
pub bounding_poly: ::core::option::Option<super::BoundingPoly>,
/// The type of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in document.
#[prost(enumeration = "layout::TextSegmentType", tag = "4")]
pub text_segment_type: i32,
}
/// Nested message and enum types in `Layout`.
pub mod layout {
/// The type of TextSegment in the context of the original document.
#[derive(
Clone,
Copy,
Debug,
PartialEq,
Eq,
Hash,
PartialOrd,
Ord,
::prost::Enumeration
)]
#[repr(i32)]
pub enum TextSegmentType {
/// Should not be used.
Unspecified = 0,
/// The text segment is a token. e.g. word.
Token = 1,
/// The text segment is a paragraph.
Paragraph = 2,
/// The text segment is a form field.
FormField = 3,
/// The text segment is the name part of a form field. It will be treated
/// as child of another FORM_FIELD TextSegment if its span is subspan of
/// another TextSegment with type FORM_FIELD.
FormFieldName = 4,
/// The text segment is the text content part of a form field. It will be
/// treated as child of another FORM_FIELD TextSegment if its span is
/// subspan of another TextSegment with type FORM_FIELD.
FormFieldContents = 5,
/// The text segment is a whole table, including headers, and all rows.
Table = 6,
/// The text segment is a table's headers. It will be treated as child of
/// another TABLE TextSegment if its span is subspan of another TextSegment
/// with type TABLE.
TableHeader = 7,
/// The text segment is a row in table. It will be treated as child of
/// another TABLE TextSegment if its span is subspan of another TextSegment
/// with type TABLE.
TableRow = 8,
/// The text segment is a cell in table. It will be treated as child of
/// another TABLE_ROW TextSegment if its span is subspan of another
/// TextSegment with type TABLE_ROW.
TableCell = 9,
}
impl TextSegmentType {
/// String value of the enum field names used in the ProtoBuf definition.
///
/// The values are not transformed in any way and thus are considered stable
/// (if the ProtoBuf definition does not change) and safe for programmatic use.
pub fn as_str_name(&self) -> &'static str {
match self {
TextSegmentType::Unspecified => "TEXT_SEGMENT_TYPE_UNSPECIFIED",
TextSegmentType::Token => "TOKEN",
TextSegmentType::Paragraph => "PARAGRAPH",
TextSegmentType::FormField => "FORM_FIELD",
TextSegmentType::FormFieldName => "FORM_FIELD_NAME",
TextSegmentType::FormFieldContents => "FORM_FIELD_CONTENTS",
TextSegmentType::Table => "TABLE",
TextSegmentType::TableHeader => "TABLE_HEADER",
TextSegmentType::TableRow => "TABLE_ROW",
TextSegmentType::TableCell => "TABLE_CELL",
}
}
/// Creates an enum from field names used in the ProtoBuf definition.
pub fn from_str_name(value: &str) -> ::core::option::Option<Self> {
match value {
"TEXT_SEGMENT_TYPE_UNSPECIFIED" => Some(Self::Unspecified),
"TOKEN" => Some(Self::Token),
"PARAGRAPH" => Some(Self::Paragraph),
"FORM_FIELD" => Some(Self::FormField),
"FORM_FIELD_NAME" => Some(Self::FormFieldName),
"FORM_FIELD_CONTENTS" => Some(Self::FormFieldContents),
"TABLE" => Some(Self::Table),
"TABLE_HEADER" => Some(Self::TableHeader),
"TABLE_ROW" => Some(Self::TableRow),
"TABLE_CELL" => Some(Self::TableCell),
_ => None,
}
}
}
}
}
/// Example data used for training or prediction.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExamplePayload {
/// Required. The example data.
#[prost(oneof = "example_payload::Payload", tags = "1, 2, 4")]
pub payload: ::core::option::Option<example_payload::Payload>,
}
/// Nested message and enum types in `ExamplePayload`.
pub mod example_payload {
/// Required. The example data.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Payload {
/// Example image.
#[prost(message, tag = "1")]
Image(super::Image),
/// Example text.
#[prost(message, tag = "2")]
TextSnippet(super::TextSnippet),
/// Example document.
#[prost(message, tag = "4")]
Document(super::Document),
}
}
/// Dataset metadata that is specific to translation.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TranslationDatasetMetadata {
/// Required. The BCP-47 language code of the source language.
#[prost(string, tag = "1")]
pub source_language_code: ::prost::alloc::string::String,
/// Required. The BCP-47 language code of the target language.
#[prost(string, tag = "2")]
pub target_language_code: ::prost::alloc::string::String,
}
/// Evaluation metrics for the dataset.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TranslationEvaluationMetrics {
/// Output only. BLEU score.
#[prost(double, tag = "1")]
pub bleu_score: f64,
/// Output only. BLEU score for base model.
#[prost(double, tag = "2")]
pub base_bleu_score: f64,
}
/// Model metadata that is specific to translation.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TranslationModelMetadata {
/// The resource name of the model to use as a baseline to train the custom
/// model. If unset, we use the default base model provided by Google
/// Translate. Format:
/// `projects/{project_id}/locations/{location_id}/models/{model_id}`
#[prost(string, tag = "1")]
pub base_model: ::prost::alloc::string::String,
/// Output only. Inferred from the dataset.
/// The source language (The BCP-47 language code) that is used for training.
#[prost(string, tag = "2")]
pub source_language_code: ::prost::alloc::string::String,
/// Output only. The target language (The BCP-47 language code) that is used
/// for training.
#[prost(string, tag = "3")]
pub target_language_code: ::prost::alloc::string::String,
}
/// Annotation details specific to translation.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TranslationAnnotation {
/// Output only . The translated content.
#[prost(message, optional, tag = "1")]
pub translated_content: ::core::option::Option<TextSnippet>,
}
/// Contains annotation details specific to classification.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ClassificationAnnotation {
/// Output only. A confidence estimate between 0.0 and 1.0. A higher value
/// means greater confidence that the annotation is positive. If a user
/// approves an annotation as negative or positive, the score value remains
/// unchanged. If a user creates an annotation, the score is 0 for negative or
/// 1 for positive.
#[prost(float, tag = "1")]
pub score: f32,
}
/// Model evaluation metrics for classification problems.
/// Note: For Video Classification this metrics only describe quality of the
/// Video Classification predictions of "segment_classification" type.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ClassificationEvaluationMetrics {
/// Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
/// for the overall evaluation.
#[prost(float, tag = "1")]
pub au_prc: f32,
/// Output only. The Area Under Receiver Operating Characteristic curve metric.
/// Micro-averaged for the overall evaluation.
#[prost(float, tag = "6")]
pub au_roc: f32,
/// Output only. The Log Loss metric.
#[prost(float, tag = "7")]
pub log_loss: f32,
/// Output only. Metrics for each confidence_threshold in
/// 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
/// position_threshold = INT32_MAX_VALUE.
/// ROC and precision-recall curves, and other aggregated metrics are derived
/// from them. The confidence metrics entries may also be supplied for
/// additional values of position_threshold, but from these no aggregated
/// metrics are computed.
#[prost(message, repeated, tag = "3")]
pub confidence_metrics_entry: ::prost::alloc::vec::Vec<
classification_evaluation_metrics::ConfidenceMetricsEntry,
>,
/// Output only. Confusion matrix of the evaluation.
/// Only set for MULTICLASS classification problems where number
/// of labels is no more than 10.
/// Only set for model level evaluation, not for evaluation per label.
#[prost(message, optional, tag = "4")]
pub confusion_matrix: ::core::option::Option<
classification_evaluation_metrics::ConfusionMatrix,
>,
/// Output only. The annotation spec ids used for this evaluation.
#[prost(string, repeated, tag = "5")]
pub annotation_spec_id: ::prost::alloc::vec::Vec<::prost::alloc::string::String>,
}
/// Nested message and enum types in `ClassificationEvaluationMetrics`.
pub mod classification_evaluation_metrics {
/// Metrics for a single confidence threshold.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ConfidenceMetricsEntry {
/// Output only. Metrics are computed with an assumption that the model
/// never returns predictions with score lower than this value.
#[prost(float, tag = "1")]
pub confidence_threshold: f32,
/// Output only. Metrics are computed with an assumption that the model
/// always returns at most this many predictions (ordered by their score,
/// descendingly), but they all still need to meet the confidence_threshold.
#[prost(int32, tag = "14")]
pub position_threshold: i32,
/// Output only. Recall (True Positive Rate) for the given confidence
/// threshold.
#[prost(float, tag = "2")]
pub recall: f32,
/// Output only. Precision for the given confidence threshold.
#[prost(float, tag = "3")]
pub precision: f32,
/// Output only. False Positive Rate for the given confidence threshold.
#[prost(float, tag = "8")]
pub false_positive_rate: f32,
/// Output only. The harmonic mean of recall and precision.
#[prost(float, tag = "4")]
pub f1_score: f32,
/// Output only. The Recall (True Positive Rate) when only considering the
/// label that has the highest prediction score and not below the confidence
/// threshold for each example.
#[prost(float, tag = "5")]
pub recall_at1: f32,
/// Output only. The precision when only considering the label that has the
/// highest prediction score and not below the confidence threshold for each
/// example.
#[prost(float, tag = "6")]
pub precision_at1: f32,
/// Output only. The False Positive Rate when only considering the label that
/// has the highest prediction score and not below the confidence threshold
/// for each example.
#[prost(float, tag = "9")]
pub false_positive_rate_at1: f32,
/// Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
#[prost(float, tag = "7")]
pub f1_score_at1: f32,
/// Output only. The number of model created labels that match a ground truth
/// label.
#[prost(int64, tag = "10")]
pub true_positive_count: i64,
/// Output only. The number of model created labels that do not match a
/// ground truth label.
#[prost(int64, tag = "11")]
pub false_positive_count: i64,
/// Output only. The number of ground truth labels that are not matched
/// by a model created label.
#[prost(int64, tag = "12")]
pub false_negative_count: i64,
/// Output only. The number of labels that were not created by the model,
/// but if they would, they would not match a ground truth label.
#[prost(int64, tag = "13")]
pub true_negative_count: i64,
}
/// Confusion matrix of the model running the classification.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ConfusionMatrix {
/// Output only. IDs of the annotation specs used in the confusion matrix.
/// For Tables CLASSIFICATION
/// [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]
/// only list of [annotation_spec_display_name-s][] is populated.
#[prost(string, repeated, tag = "1")]
pub annotation_spec_id: ::prost::alloc::vec::Vec<::prost::alloc::string::String>,
/// Output only. Display name of the annotation specs used in the confusion
/// matrix, as they were at the moment of the evaluation. For Tables
/// CLASSIFICATION
/// [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type],
/// distinct values of the target column at the moment of the model
/// evaluation are populated here.
#[prost(string, repeated, tag = "3")]
pub display_name: ::prost::alloc::vec::Vec<::prost::alloc::string::String>,
/// Output only. Rows in the confusion matrix. The number of rows is equal to
/// the size of `annotation_spec_id`.
/// `row\[i\].example_count\[j\]` is the number of examples that have ground
/// truth of the `annotation_spec_id\[i\]` and are predicted as
/// `annotation_spec_id\[j\]` by the model being evaluated.
#[prost(message, repeated, tag = "2")]
pub row: ::prost::alloc::vec::Vec<confusion_matrix::Row>,
}
/// Nested message and enum types in `ConfusionMatrix`.
pub mod confusion_matrix {
/// Output only. A row in the confusion matrix.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct Row {
/// Output only. Value of the specific cell in the confusion matrix.
/// The number of values each row has (i.e. the length of the row) is equal
/// to the length of the `annotation_spec_id` field or, if that one is not
/// populated, length of the [display_name][google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
#[prost(int32, repeated, tag = "1")]
pub example_count: ::prost::alloc::vec::Vec<i32>,
}
}
}
/// Type of the classification problem.
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash, PartialOrd, Ord, ::prost::Enumeration)]
#[repr(i32)]
pub enum ClassificationType {
/// An un-set value of this enum.
Unspecified = 0,
/// At most one label is allowed per example.
Multiclass = 1,
/// Multiple labels are allowed for one example.
Multilabel = 2,
}
impl ClassificationType {
/// String value of the enum field names used in the ProtoBuf definition.
///
/// The values are not transformed in any way and thus are considered stable
/// (if the ProtoBuf definition does not change) and safe for programmatic use.
pub fn as_str_name(&self) -> &'static str {
match self {
ClassificationType::Unspecified => "CLASSIFICATION_TYPE_UNSPECIFIED",
ClassificationType::Multiclass => "MULTICLASS",
ClassificationType::Multilabel => "MULTILABEL",
}
}
/// Creates an enum from field names used in the ProtoBuf definition.
pub fn from_str_name(value: &str) -> ::core::option::Option<Self> {
match value {
"CLASSIFICATION_TYPE_UNSPECIFIED" => Some(Self::Unspecified),
"MULTICLASS" => Some(Self::Multiclass),
"MULTILABEL" => Some(Self::Multilabel),
_ => None,
}
}
}
/// Dataset metadata that is specific to image classification.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ImageClassificationDatasetMetadata {
/// Required. Type of the classification problem.
#[prost(enumeration = "ClassificationType", tag = "1")]
pub classification_type: i32,
}
/// Dataset metadata specific to image object detection.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ImageObjectDetectionDatasetMetadata {}
/// Model metadata for image classification.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ImageClassificationModelMetadata {
/// Optional. The ID of the `base` model. If it is specified, the new model
/// will be created based on the `base` model. Otherwise, the new model will be
/// created from scratch. The `base` model must be in the same
/// `project` and `location` as the new model to create, and have the same
/// `model_type`.
#[prost(string, tag = "1")]
pub base_model_id: ::prost::alloc::string::String,
/// Optional. The train budget of creating this model, expressed in milli node
/// hours i.e. 1,000 value in this field means 1 node hour. The actual
/// `train_cost` will be equal or less than this value. If further model
/// training ceases to provide any improvements, it will stop without using
/// full budget and the stop_reason will be `MODEL_CONVERGED`.
/// Note, node_hour = actual_hour * number_of_nodes_invovled.
/// For model type `cloud`(default), the train budget must be between 8,000
/// and 800,000 milli node hours, inclusive. The default value is 192, 000
/// which represents one day in wall time. For model type
/// `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`,
/// `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`,
/// `mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000
/// and 100,000 milli node hours, inclusive. The default value is 24, 000 which
/// represents one day in wall time.
#[prost(int64, tag = "16")]
pub train_budget_milli_node_hours: i64,
/// Output only. The actual train cost of creating this model, expressed in
/// milli node hours, i.e. 1,000 value in this field means 1 node hour.
/// Guaranteed to not exceed the train budget.
#[prost(int64, tag = "17")]
pub train_cost_milli_node_hours: i64,
/// Output only. The reason that this create model operation stopped,
/// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
#[prost(string, tag = "5")]
pub stop_reason: ::prost::alloc::string::String,
/// Optional. Type of the model. The available values are:
/// * `cloud` - Model to be used via prediction calls to AutoML API.
/// This is the default value.
/// * `mobile-low-latency-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
/// with TensorFlow afterwards. Expected to have low latency, but
/// may have lower prediction quality than other models.
/// * `mobile-versatile-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
/// with TensorFlow afterwards.
/// * `mobile-high-accuracy-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
/// with TensorFlow afterwards. Expected to have a higher
/// latency, but should also have a higher prediction quality
/// than other models.
/// * `mobile-core-ml-low-latency-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
/// ML afterwards. Expected to have low latency, but may have
/// lower prediction quality than other models.
/// * `mobile-core-ml-versatile-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
/// ML afterwards.
/// * `mobile-core-ml-high-accuracy-1` - A model that, in addition to
/// providing prediction via AutoML API, can also be exported
/// (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
/// Core ML afterwards. Expected to have a higher latency, but
/// should also have a higher prediction quality than other
/// models.
#[prost(string, tag = "7")]
pub model_type: ::prost::alloc::string::String,
/// Output only. An approximate number of online prediction QPS that can
/// be supported by this model per each node on which it is deployed.
#[prost(double, tag = "13")]
pub node_qps: f64,
/// Output only. The number of nodes this model is deployed on. A node is an
/// abstraction of a machine resource, which can handle online prediction QPS
/// as given in the node_qps field.
#[prost(int64, tag = "14")]
pub node_count: i64,
}
/// Model metadata specific to image object detection.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ImageObjectDetectionModelMetadata {
/// Optional. Type of the model. The available values are:
/// * `cloud-high-accuracy-1` - (default) A model to be used via prediction
/// calls to AutoML API. Expected to have a higher latency, but
/// should also have a higher prediction quality than other
/// models.
/// * `cloud-low-latency-1` - A model to be used via prediction
/// calls to AutoML API. Expected to have low latency, but may
/// have lower prediction quality than other models.
/// * `mobile-low-latency-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
/// with TensorFlow afterwards. Expected to have low latency, but
/// may have lower prediction quality than other models.
/// * `mobile-versatile-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
/// with TensorFlow afterwards.
/// * `mobile-high-accuracy-1` - A model that, in addition to providing
/// prediction via AutoML API, can also be exported (see
/// [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
/// with TensorFlow afterwards. Expected to have a higher
/// latency, but should also have a higher prediction quality
/// than other models.
#[prost(string, tag = "1")]
pub model_type: ::prost::alloc::string::String,
/// Output only. The number of nodes this model is deployed on. A node is an
/// abstraction of a machine resource, which can handle online prediction QPS
/// as given in the qps_per_node field.
#[prost(int64, tag = "3")]
pub node_count: i64,
/// Output only. An approximate number of online prediction QPS that can
/// be supported by this model per each node on which it is deployed.
#[prost(double, tag = "4")]
pub node_qps: f64,
/// Output only. The reason that this create model operation stopped,
/// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
#[prost(string, tag = "5")]
pub stop_reason: ::prost::alloc::string::String,
/// Optional. The train budget of creating this model, expressed in milli node
/// hours i.e. 1,000 value in this field means 1 node hour. The actual
/// `train_cost` will be equal or less than this value. If further model
/// training ceases to provide any improvements, it will stop without using
/// full budget and the stop_reason will be `MODEL_CONVERGED`.
/// Note, node_hour = actual_hour * number_of_nodes_invovled.
/// For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
/// the train budget must be between 20,000 and 900,000 milli node hours,
/// inclusive. The default value is 216, 000 which represents one day in
/// wall time.
/// For model type `mobile-low-latency-1`, `mobile-versatile-1`,
/// `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
/// `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
/// budget must be between 1,000 and 100,000 milli node hours, inclusive.
/// The default value is 24, 000 which represents one day in wall time.
#[prost(int64, tag = "6")]
pub train_budget_milli_node_hours: i64,
/// Output only. The actual train cost of creating this model, expressed in
/// milli node hours, i.e. 1,000 value in this field means 1 node hour.
/// Guaranteed to not exceed the train budget.
#[prost(int64, tag = "7")]
pub train_cost_milli_node_hours: i64,
}
/// Model deployment metadata specific to Image Classification.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ImageClassificationModelDeploymentMetadata {
/// Input only. The number of nodes to deploy the model on. A node is an
/// abstraction of a machine resource, which can handle online prediction QPS
/// as given in the model's
/// [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
/// Must be between 1 and 100, inclusive on both ends.
#[prost(int64, tag = "1")]
pub node_count: i64,
}
/// Model deployment metadata specific to Image Object Detection.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ImageObjectDetectionModelDeploymentMetadata {
/// Input only. The number of nodes to deploy the model on. A node is an
/// abstraction of a machine resource, which can handle online prediction QPS
/// as given in the model's
/// [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
/// Must be between 1 and 100, inclusive on both ends.
#[prost(int64, tag = "1")]
pub node_count: i64,
}
/// Dataset metadata for classification.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextClassificationDatasetMetadata {
/// Required. Type of the classification problem.
#[prost(enumeration = "ClassificationType", tag = "1")]
pub classification_type: i32,
}
/// Model metadata that is specific to text classification.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextClassificationModelMetadata {
/// Output only. Classification type of the dataset used to train this model.
#[prost(enumeration = "ClassificationType", tag = "3")]
pub classification_type: i32,
}
/// Dataset metadata that is specific to text extraction
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextExtractionDatasetMetadata {}
/// Model metadata that is specific to text extraction.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextExtractionModelMetadata {}
/// Dataset metadata for text sentiment.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextSentimentDatasetMetadata {
/// Required. A sentiment is expressed as an integer ordinal, where higher value
/// means a more positive sentiment. The range of sentiments that will be used
/// is between 0 and sentiment_max (inclusive on both ends), and all the values
/// in the range must be represented in the dataset before a model can be
/// created.
/// sentiment_max value must be between 1 and 10 (inclusive).
#[prost(int32, tag = "1")]
pub sentiment_max: i32,
}
/// Model metadata that is specific to text sentiment.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextSentimentModelMetadata {}
/// API proto representing a trained machine learning model.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct Model {
/// Output only. Resource name of the model.
/// Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The name of the model to show in the interface. The name can be
/// up to 32 characters long and can consist only of ASCII Latin letters A-Z
/// and a-z, underscores
/// (_), and ASCII digits 0-9. It must start with a letter.
#[prost(string, tag = "2")]
pub display_name: ::prost::alloc::string::String,
/// Required. The resource ID of the dataset used to create the model. The dataset must
/// come from the same ancestor project and location.
#[prost(string, tag = "3")]
pub dataset_id: ::prost::alloc::string::String,
/// Output only. Timestamp when the model training finished and can be used for prediction.
#[prost(message, optional, tag = "7")]
pub create_time: ::core::option::Option<::prost_types::Timestamp>,
/// Output only. Timestamp when this model was last updated.
#[prost(message, optional, tag = "11")]
pub update_time: ::core::option::Option<::prost_types::Timestamp>,
/// Output only. Deployment state of the model. A model can only serve
/// prediction requests after it gets deployed.
#[prost(enumeration = "model::DeploymentState", tag = "8")]
pub deployment_state: i32,
/// Used to perform a consistent read-modify-write updates. If not set, a blind
/// "overwrite" update happens.
#[prost(string, tag = "10")]
pub etag: ::prost::alloc::string::String,
/// Optional. The labels with user-defined metadata to organize your model.
///
/// Label keys and values can be no longer than 64 characters
/// (Unicode codepoints), can only contain lowercase letters, numeric
/// characters, underscores and dashes. International characters are allowed.
/// Label values are optional. Label keys must start with a letter.
///
/// See <https://goo.gl/xmQnxf> for more information on and examples of labels.
#[prost(btree_map = "string, string", tag = "34")]
pub labels: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
/// Required.
/// The model metadata that is specific to the problem type.
/// Must match the metadata type of the dataset used to train the model.
#[prost(oneof = "model::ModelMetadata", tags = "15, 13, 14, 20, 19, 22")]
pub model_metadata: ::core::option::Option<model::ModelMetadata>,
}
/// Nested message and enum types in `Model`.
pub mod model {
/// Deployment state of the model.
#[derive(
Clone,
Copy,
Debug,
PartialEq,
Eq,
Hash,
PartialOrd,
Ord,
::prost::Enumeration
)]
#[repr(i32)]
pub enum DeploymentState {
/// Should not be used, an un-set enum has this value by default.
Unspecified = 0,
/// Model is deployed.
Deployed = 1,
/// Model is not deployed.
Undeployed = 2,
}
impl DeploymentState {
/// String value of the enum field names used in the ProtoBuf definition.
///
/// The values are not transformed in any way and thus are considered stable
/// (if the ProtoBuf definition does not change) and safe for programmatic use.
pub fn as_str_name(&self) -> &'static str {
match self {
DeploymentState::Unspecified => "DEPLOYMENT_STATE_UNSPECIFIED",
DeploymentState::Deployed => "DEPLOYED",
DeploymentState::Undeployed => "UNDEPLOYED",
}
}
/// Creates an enum from field names used in the ProtoBuf definition.
pub fn from_str_name(value: &str) -> ::core::option::Option<Self> {
match value {
"DEPLOYMENT_STATE_UNSPECIFIED" => Some(Self::Unspecified),
"DEPLOYED" => Some(Self::Deployed),
"UNDEPLOYED" => Some(Self::Undeployed),
_ => None,
}
}
}
/// Required.
/// The model metadata that is specific to the problem type.
/// Must match the metadata type of the dataset used to train the model.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum ModelMetadata {
/// Metadata for translation models.
#[prost(message, tag = "15")]
TranslationModelMetadata(super::TranslationModelMetadata),
/// Metadata for image classification models.
#[prost(message, tag = "13")]
ImageClassificationModelMetadata(super::ImageClassificationModelMetadata),
/// Metadata for text classification models.
#[prost(message, tag = "14")]
TextClassificationModelMetadata(super::TextClassificationModelMetadata),
/// Metadata for image object detection models.
#[prost(message, tag = "20")]
ImageObjectDetectionModelMetadata(super::ImageObjectDetectionModelMetadata),
/// Metadata for text extraction models.
#[prost(message, tag = "19")]
TextExtractionModelMetadata(super::TextExtractionModelMetadata),
/// Metadata for text sentiment models.
#[prost(message, tag = "22")]
TextSentimentModelMetadata(super::TextSentimentModelMetadata),
}
}
/// Annotation for identifying spans of text.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TextExtractionAnnotation {
/// Output only. A confidence estimate between 0.0 and 1.0. A higher value
/// means greater confidence in correctness of the annotation.
#[prost(float, tag = "1")]
pub score: f32,
/// Required. Text extraction annotations can either be a text segment or a
/// text relation.
#[prost(oneof = "text_extraction_annotation::Annotation", tags = "3")]
pub annotation: ::core::option::Option<text_extraction_annotation::Annotation>,
}
/// Nested message and enum types in `TextExtractionAnnotation`.
pub mod text_extraction_annotation {
/// Required. Text extraction annotations can either be a text segment or a
/// text relation.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Annotation {
/// An entity annotation will set this, which is the part of the original
/// text to which the annotation pertains.
#[prost(message, tag = "3")]
TextSegment(super::TextSegment),
}
}
/// Model evaluation metrics for text extraction problems.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TextExtractionEvaluationMetrics {
/// Output only. The Area under precision recall curve metric.
#[prost(float, tag = "1")]
pub au_prc: f32,
/// Output only. Metrics that have confidence thresholds.
/// Precision-recall curve can be derived from it.
#[prost(message, repeated, tag = "2")]
pub confidence_metrics_entries: ::prost::alloc::vec::Vec<
text_extraction_evaluation_metrics::ConfidenceMetricsEntry,
>,
}
/// Nested message and enum types in `TextExtractionEvaluationMetrics`.
pub mod text_extraction_evaluation_metrics {
/// Metrics for a single confidence threshold.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ConfidenceMetricsEntry {
/// Output only. The confidence threshold value used to compute the metrics.
/// Only annotations with score of at least this threshold are considered to
/// be ones the model would return.
#[prost(float, tag = "1")]
pub confidence_threshold: f32,
/// Output only. Recall under the given confidence threshold.
#[prost(float, tag = "3")]
pub recall: f32,
/// Output only. Precision under the given confidence threshold.
#[prost(float, tag = "4")]
pub precision: f32,
/// Output only. The harmonic mean of recall and precision.
#[prost(float, tag = "5")]
pub f1_score: f32,
}
}
/// Contains annotation details specific to text sentiment.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct TextSentimentAnnotation {
/// Output only. The sentiment with the semantic, as given to the
/// [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] when populating the dataset from which the model used
/// for the prediction had been trained.
/// The sentiment values are between 0 and
/// Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive),
/// with higher value meaning more positive sentiment. They are completely
/// relative, i.e. 0 means least positive sentiment and sentiment_max means
/// the most positive from the sentiments present in the train data. Therefore
/// e.g. if train data had only negative sentiment, then sentiment_max, would
/// be still negative (although least negative).
/// The sentiment shouldn't be confused with "score" or "magnitude"
/// from the previous Natural Language Sentiment Analysis API.
#[prost(int32, tag = "1")]
pub sentiment: i32,
}
/// Model evaluation metrics for text sentiment problems.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct TextSentimentEvaluationMetrics {
/// Output only. Precision.
#[prost(float, tag = "1")]
pub precision: f32,
/// Output only. Recall.
#[prost(float, tag = "2")]
pub recall: f32,
/// Output only. The harmonic mean of recall and precision.
#[prost(float, tag = "3")]
pub f1_score: f32,
/// Output only. Mean absolute error. Only set for the overall model
/// evaluation, not for evaluation of a single annotation spec.
#[prost(float, tag = "4")]
pub mean_absolute_error: f32,
/// Output only. Mean squared error. Only set for the overall model
/// evaluation, not for evaluation of a single annotation spec.
#[prost(float, tag = "5")]
pub mean_squared_error: f32,
/// Output only. Linear weighted kappa. Only set for the overall model
/// evaluation, not for evaluation of a single annotation spec.
#[prost(float, tag = "6")]
pub linear_kappa: f32,
/// Output only. Quadratic weighted kappa. Only set for the overall model
/// evaluation, not for evaluation of a single annotation spec.
#[prost(float, tag = "7")]
pub quadratic_kappa: f32,
/// Output only. Confusion matrix of the evaluation.
/// Only set for the overall model evaluation, not for evaluation of a single
/// annotation spec.
#[prost(message, optional, tag = "8")]
pub confusion_matrix: ::core::option::Option<
classification_evaluation_metrics::ConfusionMatrix,
>,
}
/// Contains annotation information that is relevant to AutoML.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct AnnotationPayload {
/// Output only . The resource ID of the annotation spec that
/// this annotation pertains to. The annotation spec comes from either an
/// ancestor dataset, or the dataset that was used to train the model in use.
#[prost(string, tag = "1")]
pub annotation_spec_id: ::prost::alloc::string::String,
/// Output only. The value of
/// [display_name][google.cloud.automl.v1.AnnotationSpec.display_name]
/// when the model was trained. Because this field returns a value at model
/// training time, for different models trained using the same dataset, the
/// returned value could be different as model owner could update the
/// `display_name` between any two model training.
#[prost(string, tag = "5")]
pub display_name: ::prost::alloc::string::String,
/// Output only . Additional information about the annotation
/// specific to the AutoML domain.
#[prost(oneof = "annotation_payload::Detail", tags = "2, 3, 4, 6, 7")]
pub detail: ::core::option::Option<annotation_payload::Detail>,
}
/// Nested message and enum types in `AnnotationPayload`.
pub mod annotation_payload {
/// Output only . Additional information about the annotation
/// specific to the AutoML domain.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Detail {
/// Annotation details for translation.
#[prost(message, tag = "2")]
Translation(super::TranslationAnnotation),
/// Annotation details for content or image classification.
#[prost(message, tag = "3")]
Classification(super::ClassificationAnnotation),
/// Annotation details for image object detection.
#[prost(message, tag = "4")]
ImageObjectDetection(super::ImageObjectDetectionAnnotation),
/// Annotation details for text extraction.
#[prost(message, tag = "6")]
TextExtraction(super::TextExtractionAnnotation),
/// Annotation details for text sentiment.
#[prost(message, tag = "7")]
TextSentiment(super::TextSentimentAnnotation),
}
}
/// Request message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct PredictRequest {
/// Required. Name of the model requested to serve the prediction.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. Payload to perform a prediction on. The payload must match the
/// problem type that the model was trained to solve.
#[prost(message, optional, tag = "2")]
pub payload: ::core::option::Option<ExamplePayload>,
/// Additional domain-specific parameters, any string must be up to 25000
/// characters long.
///
/// AutoML Vision Classification
///
/// `score_threshold`
/// : (float) A value from 0.0 to 1.0. When the model
/// makes predictions for an image, it will only produce results that have
/// at least this confidence score. The default is 0.5.
///
/// AutoML Vision Object Detection
///
/// `score_threshold`
/// : (float) When Model detects objects on the image,
/// it will only produce bounding boxes which have at least this
/// confidence score. Value in 0 to 1 range, default is 0.5.
///
/// `max_bounding_box_count`
/// : (int64) The maximum number of bounding
/// boxes returned. The default is 100. The
/// number of returned bounding boxes might be limited by the server.
///
/// AutoML Tables
///
/// `feature_importance`
/// : (boolean) Whether
/// [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
/// is populated in the returned list of
/// [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]
/// objects. The default is false.
#[prost(btree_map = "string, string", tag = "3")]
pub params: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
}
/// Response message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct PredictResponse {
/// Prediction result.
/// AutoML Translation and AutoML Natural Language Sentiment Analysis
/// return precisely one payload.
#[prost(message, repeated, tag = "1")]
pub payload: ::prost::alloc::vec::Vec<AnnotationPayload>,
/// The preprocessed example that AutoML actually makes prediction on.
/// Empty if AutoML does not preprocess the input example.
///
/// For AutoML Natural Language (Classification, Entity Extraction, and
/// Sentiment Analysis), if the input is a document, the recognized text is
/// returned in the
/// [document_text][google.cloud.automl.v1.Document.document_text]
/// property.
#[prost(message, optional, tag = "3")]
pub preprocessed_input: ::core::option::Option<ExamplePayload>,
/// Additional domain-specific prediction response metadata.
///
/// AutoML Vision Object Detection
///
/// `max_bounding_box_count`
/// : (int64) The maximum number of bounding boxes to return per image.
///
/// AutoML Natural Language Sentiment Analysis
///
/// `sentiment_score`
/// : (float, deprecated) A value between -1 and 1,
/// -1 maps to least positive sentiment, while 1 maps to the most positive
/// one and the higher the score, the more positive the sentiment in the
/// document is. Yet these values are relative to the training data, so
/// e.g. if all data was positive then -1 is also positive (though
/// the least).
/// `sentiment_score` is not the same as "score" and "magnitude"
/// from Sentiment Analysis in the Natural Language API.
#[prost(btree_map = "string, string", tag = "2")]
pub metadata: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
}
/// Request message for [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BatchPredictRequest {
/// Required. Name of the model requested to serve the batch prediction.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The input configuration for batch prediction.
#[prost(message, optional, tag = "3")]
pub input_config: ::core::option::Option<BatchPredictInputConfig>,
/// Required. The Configuration specifying where output predictions should
/// be written.
#[prost(message, optional, tag = "4")]
pub output_config: ::core::option::Option<BatchPredictOutputConfig>,
/// Additional domain-specific parameters for the predictions, any string must
/// be up to 25000 characters long.
///
/// AutoML Natural Language Classification
///
/// `score_threshold`
/// : (float) A value from 0.0 to 1.0. When the model
/// makes predictions for a text snippet, it will only produce results
/// that have at least this confidence score. The default is 0.5.
///
///
/// AutoML Vision Classification
///
/// `score_threshold`
/// : (float) A value from 0.0 to 1.0. When the model
/// makes predictions for an image, it will only produce results that
/// have at least this confidence score. The default is 0.5.
///
/// AutoML Vision Object Detection
///
/// `score_threshold`
/// : (float) When Model detects objects on the image,
/// it will only produce bounding boxes which have at least this
/// confidence score. Value in 0 to 1 range, default is 0.5.
///
/// `max_bounding_box_count`
/// : (int64) The maximum number of bounding
/// boxes returned per image. The default is 100, the
/// number of bounding boxes returned might be limited by the server.
/// AutoML Video Intelligence Classification
///
/// `score_threshold`
/// : (float) A value from 0.0 to 1.0. When the model
/// makes predictions for a video, it will only produce results that
/// have at least this confidence score. The default is 0.5.
///
/// `segment_classification`
/// : (boolean) Set to true to request
/// segment-level classification. AutoML Video Intelligence returns
/// labels and their confidence scores for the entire segment of the
/// video that user specified in the request configuration.
/// The default is true.
///
/// `shot_classification`
/// : (boolean) Set to true to request shot-level
/// classification. AutoML Video Intelligence determines the boundaries
/// for each camera shot in the entire segment of the video that user
/// specified in the request configuration. AutoML Video Intelligence
/// then returns labels and their confidence scores for each detected
/// shot, along with the start and end time of the shot.
/// The default is false.
///
/// WARNING: Model evaluation is not done for this classification type,
/// the quality of it depends on training data, but there are no metrics
/// provided to describe that quality.
///
/// `1s_interval_classification`
/// : (boolean) Set to true to request
/// classification for a video at one-second intervals. AutoML Video
/// Intelligence returns labels and their confidence scores for each
/// second of the entire segment of the video that user specified in the
/// request configuration. The default is false.
///
/// WARNING: Model evaluation is not done for this classification
/// type, the quality of it depends on training data, but there are no
/// metrics provided to describe that quality.
///
/// AutoML Video Intelligence Object Tracking
///
/// `score_threshold`
/// : (float) When Model detects objects on video frames,
/// it will only produce bounding boxes which have at least this
/// confidence score. Value in 0 to 1 range, default is 0.5.
///
/// `max_bounding_box_count`
/// : (int64) The maximum number of bounding
/// boxes returned per image. The default is 100, the
/// number of bounding boxes returned might be limited by the server.
///
/// `min_bounding_box_size`
/// : (float) Only bounding boxes with shortest edge
/// at least that long as a relative value of video frame size are
/// returned. Value in 0 to 1 range. Default is 0.
///
#[prost(btree_map = "string, string", tag = "5")]
pub params: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
}
/// Result of the Batch Predict. This message is returned in
/// [response][google.longrunning.Operation.response] of the operation returned
/// by the [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BatchPredictResult {
/// Additional domain-specific prediction response metadata.
///
/// AutoML Vision Object Detection
///
/// `max_bounding_box_count`
/// : (int64) The maximum number of bounding boxes returned per image.
///
/// AutoML Video Intelligence Object Tracking
///
/// `max_bounding_box_count`
/// : (int64) The maximum number of bounding boxes returned per frame.
#[prost(btree_map = "string, string", tag = "1")]
pub metadata: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
}
/// Generated client implementations.
pub mod prediction_service_client {
#![allow(unused_variables, dead_code, missing_docs, clippy::let_unit_value)]
use tonic::codegen::*;
use tonic::codegen::http::Uri;
/// AutoML Prediction API.
///
/// On any input that is documented to expect a string parameter in
/// snake_case or dash-case, either of those cases is accepted.
#[derive(Debug, Clone)]
pub struct PredictionServiceClient<T> {
inner: tonic::client::Grpc<T>,
}
impl<T> PredictionServiceClient<T>
where
T: tonic::client::GrpcService<tonic::body::BoxBody>,
T::Error: Into<StdError>,
T::ResponseBody: Body<Data = Bytes> + std::marker::Send + 'static,
<T::ResponseBody as Body>::Error: Into<StdError> + std::marker::Send,
{
pub fn new(inner: T) -> Self {
let inner = tonic::client::Grpc::new(inner);
Self { inner }
}
pub fn with_origin(inner: T, origin: Uri) -> Self {
let inner = tonic::client::Grpc::with_origin(inner, origin);
Self { inner }
}
pub fn with_interceptor<F>(
inner: T,
interceptor: F,
) -> PredictionServiceClient<InterceptedService<T, F>>
where
F: tonic::service::Interceptor,
T::ResponseBody: Default,
T: tonic::codegen::Service<
http::Request<tonic::body::BoxBody>,
Response = http::Response<
<T as tonic::client::GrpcService<tonic::body::BoxBody>>::ResponseBody,
>,
>,
<T as tonic::codegen::Service<
http::Request<tonic::body::BoxBody>,
>>::Error: Into<StdError> + std::marker::Send + std::marker::Sync,
{
PredictionServiceClient::new(InterceptedService::new(inner, interceptor))
}
/// Compress requests with the given encoding.
///
/// This requires the server to support it otherwise it might respond with an
/// error.
#[must_use]
pub fn send_compressed(mut self, encoding: CompressionEncoding) -> Self {
self.inner = self.inner.send_compressed(encoding);
self
}
/// Enable decompressing responses.
#[must_use]
pub fn accept_compressed(mut self, encoding: CompressionEncoding) -> Self {
self.inner = self.inner.accept_compressed(encoding);
self
}
/// Limits the maximum size of a decoded message.
///
/// Default: `4MB`
#[must_use]
pub fn max_decoding_message_size(mut self, limit: usize) -> Self {
self.inner = self.inner.max_decoding_message_size(limit);
self
}
/// Limits the maximum size of an encoded message.
///
/// Default: `usize::MAX`
#[must_use]
pub fn max_encoding_message_size(mut self, limit: usize) -> Self {
self.inner = self.inner.max_encoding_message_size(limit);
self
}
/// Perform an online prediction. The prediction result is directly
/// returned in the response.
/// Available for following ML scenarios, and their expected request payloads:
///
/// AutoML Vision Classification
///
/// * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
///
/// AutoML Vision Object Detection
///
/// * An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
///
/// AutoML Natural Language Classification
///
/// * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
/// .PDF, .TIF or .TIFF format with size upto 2MB.
///
/// AutoML Natural Language Entity Extraction
///
/// * A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document
/// in .PDF, .TIF or .TIFF format with size upto 20MB.
///
/// AutoML Natural Language Sentiment Analysis
///
/// * A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in
/// .PDF, .TIF or .TIFF format with size upto 2MB.
///
/// AutoML Translation
///
/// * A TextSnippet up to 25,000 characters, UTF-8 encoded.
///
/// AutoML Tables
///
/// * A row with column values matching
/// the columns of the model, up to 5MB. Not available for FORECASTING
/// `prediction_type`.
pub async fn predict(
&mut self,
request: impl tonic::IntoRequest<super::PredictRequest>,
) -> std::result::Result<
tonic::Response<super::PredictResponse>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.PredictionService/Predict",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new(
"google.cloud.automl.v1.PredictionService",
"Predict",
),
);
self.inner.unary(req, path, codec).await
}
/// Perform a batch prediction. Unlike the online [Predict][google.cloud.automl.v1.PredictionService.Predict], batch
/// prediction result won't be immediately available in the response. Instead,
/// a long running operation object is returned. User can poll the operation
/// result via [GetOperation][google.longrunning.Operations.GetOperation]
/// method. Once the operation is done, [BatchPredictResult][google.cloud.automl.v1.BatchPredictResult] is returned in
/// the [response][google.longrunning.Operation.response] field.
/// Available for following ML scenarios:
///
/// * AutoML Vision Classification
/// * AutoML Vision Object Detection
/// * AutoML Video Intelligence Classification
/// * AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
/// * AutoML Natural Language Entity Extraction
/// * AutoML Natural Language Sentiment Analysis
/// * AutoML Tables
pub async fn batch_predict(
&mut self,
request: impl tonic::IntoRequest<super::BatchPredictRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.PredictionService/BatchPredict",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new(
"google.cloud.automl.v1.PredictionService",
"BatchPredict",
),
);
self.inner.unary(req, path, codec).await
}
}
}
/// A workspace for solving a single, particular machine learning (ML) problem.
/// A workspace contains examples that may be annotated.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct Dataset {
/// Output only. The resource name of the dataset.
/// Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The name of the dataset to show in the interface. The name can be
/// up to 32 characters long and can consist only of ASCII Latin letters A-Z
/// and a-z, underscores
/// (_), and ASCII digits 0-9.
#[prost(string, tag = "2")]
pub display_name: ::prost::alloc::string::String,
/// User-provided description of the dataset. The description can be up to
/// 25000 characters long.
#[prost(string, tag = "3")]
pub description: ::prost::alloc::string::String,
/// Output only. The number of examples in the dataset.
#[prost(int32, tag = "21")]
pub example_count: i32,
/// Output only. Timestamp when this dataset was created.
#[prost(message, optional, tag = "14")]
pub create_time: ::core::option::Option<::prost_types::Timestamp>,
/// Used to perform consistent read-modify-write updates. If not set, a blind
/// "overwrite" update happens.
#[prost(string, tag = "17")]
pub etag: ::prost::alloc::string::String,
/// Optional. The labels with user-defined metadata to organize your dataset.
///
/// Label keys and values can be no longer than 64 characters
/// (Unicode codepoints), can only contain lowercase letters, numeric
/// characters, underscores and dashes. International characters are allowed.
/// Label values are optional. Label keys must start with a letter.
///
/// See <https://goo.gl/xmQnxf> for more information on and examples of labels.
#[prost(btree_map = "string, string", tag = "39")]
pub labels: ::prost::alloc::collections::BTreeMap<
::prost::alloc::string::String,
::prost::alloc::string::String,
>,
/// Required.
/// The dataset metadata that is specific to the problem type.
#[prost(oneof = "dataset::DatasetMetadata", tags = "23, 24, 25, 26, 28, 30")]
pub dataset_metadata: ::core::option::Option<dataset::DatasetMetadata>,
}
/// Nested message and enum types in `Dataset`.
pub mod dataset {
/// Required.
/// The dataset metadata that is specific to the problem type.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum DatasetMetadata {
/// Metadata for a dataset used for translation.
#[prost(message, tag = "23")]
TranslationDatasetMetadata(super::TranslationDatasetMetadata),
/// Metadata for a dataset used for image classification.
#[prost(message, tag = "24")]
ImageClassificationDatasetMetadata(super::ImageClassificationDatasetMetadata),
/// Metadata for a dataset used for text classification.
#[prost(message, tag = "25")]
TextClassificationDatasetMetadata(super::TextClassificationDatasetMetadata),
/// Metadata for a dataset used for image object detection.
#[prost(message, tag = "26")]
ImageObjectDetectionDatasetMetadata(super::ImageObjectDetectionDatasetMetadata),
/// Metadata for a dataset used for text extraction.
#[prost(message, tag = "28")]
TextExtractionDatasetMetadata(super::TextExtractionDatasetMetadata),
/// Metadata for a dataset used for text sentiment.
#[prost(message, tag = "30")]
TextSentimentDatasetMetadata(super::TextSentimentDatasetMetadata),
}
}
/// A definition of an annotation spec.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct AnnotationSpec {
/// Output only. Resource name of the annotation spec.
/// Form:
/// 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The name of the annotation spec to show in the interface. The name can be
/// up to 32 characters long and must match the regexp `\[a-zA-Z0-9_\]+`.
#[prost(string, tag = "2")]
pub display_name: ::prost::alloc::string::String,
/// Output only. The number of examples in the parent dataset
/// labeled by the annotation spec.
#[prost(int32, tag = "9")]
pub example_count: i32,
}
/// Metadata used across all long running operations returned by AutoML API.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct OperationMetadata {
/// Output only. Progress of operation. Range: \[0, 100\].
/// Not used currently.
#[prost(int32, tag = "13")]
pub progress_percent: i32,
/// Output only. Partial failures encountered.
/// E.g. single files that couldn't be read.
/// This field should never exceed 20 entries.
/// Status details field will contain standard GCP error details.
#[prost(message, repeated, tag = "2")]
pub partial_failures: ::prost::alloc::vec::Vec<super::super::super::rpc::Status>,
/// Output only. Time when the operation was created.
#[prost(message, optional, tag = "3")]
pub create_time: ::core::option::Option<::prost_types::Timestamp>,
/// Output only. Time when the operation was updated for the last time.
#[prost(message, optional, tag = "4")]
pub update_time: ::core::option::Option<::prost_types::Timestamp>,
/// Ouptut only. Details of specific operation. Even if this field is empty,
/// the presence allows to distinguish different types of operations.
#[prost(
oneof = "operation_metadata::Details",
tags = "8, 24, 25, 10, 30, 15, 16, 21, 22"
)]
pub details: ::core::option::Option<operation_metadata::Details>,
}
/// Nested message and enum types in `OperationMetadata`.
pub mod operation_metadata {
/// Ouptut only. Details of specific operation. Even if this field is empty,
/// the presence allows to distinguish different types of operations.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Details {
/// Details of a Delete operation.
#[prost(message, tag = "8")]
DeleteDetails(super::DeleteOperationMetadata),
/// Details of a DeployModel operation.
#[prost(message, tag = "24")]
DeployModelDetails(super::DeployModelOperationMetadata),
/// Details of an UndeployModel operation.
#[prost(message, tag = "25")]
UndeployModelDetails(super::UndeployModelOperationMetadata),
/// Details of CreateModel operation.
#[prost(message, tag = "10")]
CreateModelDetails(super::CreateModelOperationMetadata),
/// Details of CreateDataset operation.
#[prost(message, tag = "30")]
CreateDatasetDetails(super::CreateDatasetOperationMetadata),
/// Details of ImportData operation.
#[prost(message, tag = "15")]
ImportDataDetails(super::ImportDataOperationMetadata),
/// Details of BatchPredict operation.
#[prost(message, tag = "16")]
BatchPredictDetails(super::BatchPredictOperationMetadata),
/// Details of ExportData operation.
#[prost(message, tag = "21")]
ExportDataDetails(super::ExportDataOperationMetadata),
/// Details of ExportModel operation.
#[prost(message, tag = "22")]
ExportModelDetails(super::ExportModelOperationMetadata),
}
}
/// Details of operations that perform deletes of any entities.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct DeleteOperationMetadata {}
/// Details of DeployModel operation.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct DeployModelOperationMetadata {}
/// Details of UndeployModel operation.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct UndeployModelOperationMetadata {}
/// Details of CreateDataset operation.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct CreateDatasetOperationMetadata {}
/// Details of CreateModel operation.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct CreateModelOperationMetadata {}
/// Details of ImportData operation.
#[derive(Clone, Copy, PartialEq, ::prost::Message)]
pub struct ImportDataOperationMetadata {}
/// Details of ExportData operation.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExportDataOperationMetadata {
/// Output only. Information further describing this export data's output.
#[prost(message, optional, tag = "1")]
pub output_info: ::core::option::Option<
export_data_operation_metadata::ExportDataOutputInfo,
>,
}
/// Nested message and enum types in `ExportDataOperationMetadata`.
pub mod export_data_operation_metadata {
/// Further describes this export data's output.
/// Supplements
/// [OutputConfig][google.cloud.automl.v1.OutputConfig].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExportDataOutputInfo {
/// The output location to which the exported data is written.
#[prost(oneof = "export_data_output_info::OutputLocation", tags = "1")]
pub output_location: ::core::option::Option<
export_data_output_info::OutputLocation,
>,
}
/// Nested message and enum types in `ExportDataOutputInfo`.
pub mod export_data_output_info {
/// The output location to which the exported data is written.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum OutputLocation {
/// The full path of the Google Cloud Storage directory created, into which
/// the exported data is written.
#[prost(string, tag = "1")]
GcsOutputDirectory(::prost::alloc::string::String),
}
}
}
/// Details of BatchPredict operation.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BatchPredictOperationMetadata {
/// Output only. The input config that was given upon starting this
/// batch predict operation.
#[prost(message, optional, tag = "1")]
pub input_config: ::core::option::Option<BatchPredictInputConfig>,
/// Output only. Information further describing this batch predict's output.
#[prost(message, optional, tag = "2")]
pub output_info: ::core::option::Option<
batch_predict_operation_metadata::BatchPredictOutputInfo,
>,
}
/// Nested message and enum types in `BatchPredictOperationMetadata`.
pub mod batch_predict_operation_metadata {
/// Further describes this batch predict's output.
/// Supplements
/// [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct BatchPredictOutputInfo {
/// The output location into which prediction output is written.
#[prost(oneof = "batch_predict_output_info::OutputLocation", tags = "1")]
pub output_location: ::core::option::Option<
batch_predict_output_info::OutputLocation,
>,
}
/// Nested message and enum types in `BatchPredictOutputInfo`.
pub mod batch_predict_output_info {
/// The output location into which prediction output is written.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum OutputLocation {
/// The full path of the Google Cloud Storage directory created, into which
/// the prediction output is written.
#[prost(string, tag = "1")]
GcsOutputDirectory(::prost::alloc::string::String),
}
}
}
/// Details of ExportModel operation.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExportModelOperationMetadata {
/// Output only. Information further describing the output of this model
/// export.
#[prost(message, optional, tag = "2")]
pub output_info: ::core::option::Option<
export_model_operation_metadata::ExportModelOutputInfo,
>,
}
/// Nested message and enum types in `ExportModelOperationMetadata`.
pub mod export_model_operation_metadata {
/// Further describes the output of model export.
/// Supplements
/// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExportModelOutputInfo {
/// The full path of the Google Cloud Storage directory created, into which
/// the model will be exported.
#[prost(string, tag = "1")]
pub gcs_output_directory: ::prost::alloc::string::String,
}
}
/// Evaluation results of a model.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ModelEvaluation {
/// Output only. Resource name of the model evaluation.
/// Format:
/// `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Output only. The ID of the annotation spec that the model evaluation applies to. The
/// The ID is empty for the overall model evaluation.
/// For Tables annotation specs in the dataset do not exist and this ID is
/// always not set, but for CLASSIFICATION
/// [prediction_type-s][google.cloud.automl.v1.TablesModelMetadata.prediction_type]
/// the
/// [display_name][google.cloud.automl.v1.ModelEvaluation.display_name]
/// field is used.
#[prost(string, tag = "2")]
pub annotation_spec_id: ::prost::alloc::string::String,
/// Output only. The value of
/// [display_name][google.cloud.automl.v1.AnnotationSpec.display_name]
/// at the moment when the model was trained. Because this field returns a
/// value at model training time, for different models trained from the same
/// dataset, the values may differ, since display names could had been changed
/// between the two model's trainings. For Tables CLASSIFICATION
/// [prediction_type-s][google.cloud.automl.v1.TablesModelMetadata.prediction_type]
/// distinct values of the target column at the moment of the model evaluation
/// are populated here.
/// The display_name is empty for the overall model evaluation.
#[prost(string, tag = "15")]
pub display_name: ::prost::alloc::string::String,
/// Output only. Timestamp when this model evaluation was created.
#[prost(message, optional, tag = "5")]
pub create_time: ::core::option::Option<::prost_types::Timestamp>,
/// Output only. The number of examples used for model evaluation, i.e. for
/// which ground truth from time of model creation is compared against the
/// predicted annotations created by the model.
/// For overall ModelEvaluation (i.e. with annotation_spec_id not set) this is
/// the total number of all examples used for evaluation.
/// Otherwise, this is the count of examples that according to the ground
/// truth were annotated by the
/// [annotation_spec_id][google.cloud.automl.v1.ModelEvaluation.annotation_spec_id].
#[prost(int32, tag = "6")]
pub evaluated_example_count: i32,
/// Output only. Problem type specific evaluation metrics.
#[prost(oneof = "model_evaluation::Metrics", tags = "8, 9, 12, 11, 13")]
pub metrics: ::core::option::Option<model_evaluation::Metrics>,
}
/// Nested message and enum types in `ModelEvaluation`.
pub mod model_evaluation {
/// Output only. Problem type specific evaluation metrics.
#[derive(Clone, PartialEq, ::prost::Oneof)]
pub enum Metrics {
/// Model evaluation metrics for image, text, video and tables
/// classification.
/// Tables problem is considered a classification when the target column
/// is CATEGORY DataType.
#[prost(message, tag = "8")]
ClassificationEvaluationMetrics(super::ClassificationEvaluationMetrics),
/// Model evaluation metrics for translation.
#[prost(message, tag = "9")]
TranslationEvaluationMetrics(super::TranslationEvaluationMetrics),
/// Model evaluation metrics for image object detection.
#[prost(message, tag = "12")]
ImageObjectDetectionEvaluationMetrics(
super::ImageObjectDetectionEvaluationMetrics,
),
/// Evaluation metrics for text sentiment models.
#[prost(message, tag = "11")]
TextSentimentEvaluationMetrics(super::TextSentimentEvaluationMetrics),
/// Evaluation metrics for text extraction models.
#[prost(message, tag = "13")]
TextExtractionEvaluationMetrics(super::TextExtractionEvaluationMetrics),
}
}
/// Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct CreateDatasetRequest {
/// Required. The resource name of the project to create the dataset for.
#[prost(string, tag = "1")]
pub parent: ::prost::alloc::string::String,
/// Required. The dataset to create.
#[prost(message, optional, tag = "2")]
pub dataset: ::core::option::Option<Dataset>,
}
/// Request message for [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct GetDatasetRequest {
/// Required. The resource name of the dataset to retrieve.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ListDatasetsRequest {
/// Required. The resource name of the project from which to list datasets.
#[prost(string, tag = "1")]
pub parent: ::prost::alloc::string::String,
/// An expression for filtering the results of the request.
///
/// * `dataset_metadata` - for existence of the case (e.g.
/// `image_classification_dataset_metadata:*`). Some examples of using the filter are:
///
/// * `translation_dataset_metadata:*` --> The dataset has
/// `translation_dataset_metadata`.
#[prost(string, tag = "3")]
pub filter: ::prost::alloc::string::String,
/// Requested page size. Server may return fewer results than requested.
/// If unspecified, server will pick a default size.
#[prost(int32, tag = "4")]
pub page_size: i32,
/// A token identifying a page of results for the server to return
/// Typically obtained via
/// [ListDatasetsResponse.next_page_token][google.cloud.automl.v1.ListDatasetsResponse.next_page_token] of the previous
/// [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets] call.
#[prost(string, tag = "6")]
pub page_token: ::prost::alloc::string::String,
}
/// Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ListDatasetsResponse {
/// The datasets read.
#[prost(message, repeated, tag = "1")]
pub datasets: ::prost::alloc::vec::Vec<Dataset>,
/// A token to retrieve next page of results.
/// Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1.ListDatasetsRequest.page_token] to obtain that page.
#[prost(string, tag = "2")]
pub next_page_token: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct UpdateDatasetRequest {
/// Required. The dataset which replaces the resource on the server.
#[prost(message, optional, tag = "1")]
pub dataset: ::core::option::Option<Dataset>,
/// Required. The update mask applies to the resource.
#[prost(message, optional, tag = "2")]
pub update_mask: ::core::option::Option<::prost_types::FieldMask>,
}
/// Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct DeleteDatasetRequest {
/// Required. The resource name of the dataset to delete.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ImportDataRequest {
/// Required. Dataset name. Dataset must already exist. All imported
/// annotations and examples will be added.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The desired input location and its domain specific semantics,
/// if any.
#[prost(message, optional, tag = "3")]
pub input_config: ::core::option::Option<InputConfig>,
}
/// Request message for [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExportDataRequest {
/// Required. The resource name of the dataset.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The desired output location.
#[prost(message, optional, tag = "3")]
pub output_config: ::core::option::Option<OutputConfig>,
}
/// Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct GetAnnotationSpecRequest {
/// Required. The resource name of the annotation spec to retrieve.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct CreateModelRequest {
/// Required. Resource name of the parent project where the model is being created.
#[prost(string, tag = "1")]
pub parent: ::prost::alloc::string::String,
/// Required. The model to create.
#[prost(message, optional, tag = "4")]
pub model: ::core::option::Option<Model>,
}
/// Request message for [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct GetModelRequest {
/// Required. Resource name of the model.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ListModelsRequest {
/// Required. Resource name of the project, from which to list the models.
#[prost(string, tag = "1")]
pub parent: ::prost::alloc::string::String,
/// An expression for filtering the results of the request.
///
/// * `model_metadata` - for existence of the case (e.g.
/// `video_classification_model_metadata:*`).
/// * `dataset_id` - for = or !=. Some examples of using the filter are:
///
/// * `image_classification_model_metadata:*` --> The model has
/// `image_classification_model_metadata`.
/// * `dataset_id=5` --> The model was created from a dataset with ID 5.
#[prost(string, tag = "3")]
pub filter: ::prost::alloc::string::String,
/// Requested page size.
#[prost(int32, tag = "4")]
pub page_size: i32,
/// A token identifying a page of results for the server to return
/// Typically obtained via
/// [ListModelsResponse.next_page_token][google.cloud.automl.v1.ListModelsResponse.next_page_token] of the previous
/// [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels] call.
#[prost(string, tag = "6")]
pub page_token: ::prost::alloc::string::String,
}
/// Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ListModelsResponse {
/// List of models in the requested page.
#[prost(message, repeated, tag = "1")]
pub model: ::prost::alloc::vec::Vec<Model>,
/// A token to retrieve next page of results.
/// Pass to [ListModelsRequest.page_token][google.cloud.automl.v1.ListModelsRequest.page_token] to obtain that page.
#[prost(string, tag = "2")]
pub next_page_token: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct DeleteModelRequest {
/// Required. Resource name of the model being deleted.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct UpdateModelRequest {
/// Required. The model which replaces the resource on the server.
#[prost(message, optional, tag = "1")]
pub model: ::core::option::Option<Model>,
/// Required. The update mask applies to the resource.
#[prost(message, optional, tag = "2")]
pub update_mask: ::core::option::Option<::prost_types::FieldMask>,
}
/// Request message for [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct DeployModelRequest {
/// Required. Resource name of the model to deploy.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// The per-domain specific deployment parameters.
#[prost(oneof = "deploy_model_request::ModelDeploymentMetadata", tags = "2, 4")]
pub model_deployment_metadata: ::core::option::Option<
deploy_model_request::ModelDeploymentMetadata,
>,
}
/// Nested message and enum types in `DeployModelRequest`.
pub mod deploy_model_request {
/// The per-domain specific deployment parameters.
#[derive(Clone, Copy, PartialEq, ::prost::Oneof)]
pub enum ModelDeploymentMetadata {
/// Model deployment metadata specific to Image Object Detection.
#[prost(message, tag = "2")]
ImageObjectDetectionModelDeploymentMetadata(
super::ImageObjectDetectionModelDeploymentMetadata,
),
/// Model deployment metadata specific to Image Classification.
#[prost(message, tag = "4")]
ImageClassificationModelDeploymentMetadata(
super::ImageClassificationModelDeploymentMetadata,
),
}
}
/// Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct UndeployModelRequest {
/// Required. Resource name of the model to undeploy.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel].
/// Models need to be enabled for exporting, otherwise an error code will be
/// returned.
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ExportModelRequest {
/// Required. The resource name of the model to export.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
/// Required. The desired output location and configuration.
#[prost(message, optional, tag = "3")]
pub output_config: ::core::option::Option<ModelExportOutputConfig>,
}
/// Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct GetModelEvaluationRequest {
/// Required. Resource name for the model evaluation.
#[prost(string, tag = "1")]
pub name: ::prost::alloc::string::String,
}
/// Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ListModelEvaluationsRequest {
/// Required. Resource name of the model to list the model evaluations for.
/// If modelId is set as "-", this will list model evaluations from across all
/// models of the parent location.
#[prost(string, tag = "1")]
pub parent: ::prost::alloc::string::String,
/// Required. An expression for filtering the results of the request.
///
/// * `annotation_spec_id` - for =, != or existence. See example below for
/// the last.
///
/// Some examples of using the filter are:
///
/// * `annotation_spec_id!=4` --> The model evaluation was done for
/// annotation spec with ID different than 4.
/// * `NOT annotation_spec_id:*` --> The model evaluation was done for
/// aggregate of all annotation specs.
#[prost(string, tag = "3")]
pub filter: ::prost::alloc::string::String,
/// Requested page size.
#[prost(int32, tag = "4")]
pub page_size: i32,
/// A token identifying a page of results for the server to return.
/// Typically obtained via
/// [ListModelEvaluationsResponse.next_page_token][google.cloud.automl.v1.ListModelEvaluationsResponse.next_page_token] of the previous
/// [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] call.
#[prost(string, tag = "6")]
pub page_token: ::prost::alloc::string::String,
}
/// Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
#[derive(Clone, PartialEq, ::prost::Message)]
pub struct ListModelEvaluationsResponse {
/// List of model evaluations in the requested page.
#[prost(message, repeated, tag = "1")]
pub model_evaluation: ::prost::alloc::vec::Vec<ModelEvaluation>,
/// A token to retrieve next page of results.
/// Pass to the [ListModelEvaluationsRequest.page_token][google.cloud.automl.v1.ListModelEvaluationsRequest.page_token] field of a new
/// [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations] request to obtain that page.
#[prost(string, tag = "2")]
pub next_page_token: ::prost::alloc::string::String,
}
/// Generated client implementations.
pub mod auto_ml_client {
#![allow(unused_variables, dead_code, missing_docs, clippy::let_unit_value)]
use tonic::codegen::*;
use tonic::codegen::http::Uri;
/// AutoML Server API.
///
/// The resource names are assigned by the server.
/// The server never reuses names that it has created after the resources with
/// those names are deleted.
///
/// An ID of a resource is the last element of the item's resource name. For
/// `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`, then
/// the id for the item is `{dataset_id}`.
///
/// Currently the only supported `location_id` is "us-central1".
///
/// On any input that is documented to expect a string parameter in
/// snake_case or dash-case, either of those cases is accepted.
#[derive(Debug, Clone)]
pub struct AutoMlClient<T> {
inner: tonic::client::Grpc<T>,
}
impl<T> AutoMlClient<T>
where
T: tonic::client::GrpcService<tonic::body::BoxBody>,
T::Error: Into<StdError>,
T::ResponseBody: Body<Data = Bytes> + std::marker::Send + 'static,
<T::ResponseBody as Body>::Error: Into<StdError> + std::marker::Send,
{
pub fn new(inner: T) -> Self {
let inner = tonic::client::Grpc::new(inner);
Self { inner }
}
pub fn with_origin(inner: T, origin: Uri) -> Self {
let inner = tonic::client::Grpc::with_origin(inner, origin);
Self { inner }
}
pub fn with_interceptor<F>(
inner: T,
interceptor: F,
) -> AutoMlClient<InterceptedService<T, F>>
where
F: tonic::service::Interceptor,
T::ResponseBody: Default,
T: tonic::codegen::Service<
http::Request<tonic::body::BoxBody>,
Response = http::Response<
<T as tonic::client::GrpcService<tonic::body::BoxBody>>::ResponseBody,
>,
>,
<T as tonic::codegen::Service<
http::Request<tonic::body::BoxBody>,
>>::Error: Into<StdError> + std::marker::Send + std::marker::Sync,
{
AutoMlClient::new(InterceptedService::new(inner, interceptor))
}
/// Compress requests with the given encoding.
///
/// This requires the server to support it otherwise it might respond with an
/// error.
#[must_use]
pub fn send_compressed(mut self, encoding: CompressionEncoding) -> Self {
self.inner = self.inner.send_compressed(encoding);
self
}
/// Enable decompressing responses.
#[must_use]
pub fn accept_compressed(mut self, encoding: CompressionEncoding) -> Self {
self.inner = self.inner.accept_compressed(encoding);
self
}
/// Limits the maximum size of a decoded message.
///
/// Default: `4MB`
#[must_use]
pub fn max_decoding_message_size(mut self, limit: usize) -> Self {
self.inner = self.inner.max_decoding_message_size(limit);
self
}
/// Limits the maximum size of an encoded message.
///
/// Default: `usize::MAX`
#[must_use]
pub fn max_encoding_message_size(mut self, limit: usize) -> Self {
self.inner = self.inner.max_encoding_message_size(limit);
self
}
/// Creates a dataset.
pub async fn create_dataset(
&mut self,
request: impl tonic::IntoRequest<super::CreateDatasetRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/CreateDataset",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new("google.cloud.automl.v1.AutoMl", "CreateDataset"),
);
self.inner.unary(req, path, codec).await
}
/// Gets a dataset.
pub async fn get_dataset(
&mut self,
request: impl tonic::IntoRequest<super::GetDatasetRequest>,
) -> std::result::Result<tonic::Response<super::Dataset>, tonic::Status> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/GetDataset",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "GetDataset"));
self.inner.unary(req, path, codec).await
}
/// Lists datasets in a project.
pub async fn list_datasets(
&mut self,
request: impl tonic::IntoRequest<super::ListDatasetsRequest>,
) -> std::result::Result<
tonic::Response<super::ListDatasetsResponse>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/ListDatasets",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new("google.cloud.automl.v1.AutoMl", "ListDatasets"),
);
self.inner.unary(req, path, codec).await
}
/// Updates a dataset.
pub async fn update_dataset(
&mut self,
request: impl tonic::IntoRequest<super::UpdateDatasetRequest>,
) -> std::result::Result<tonic::Response<super::Dataset>, tonic::Status> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/UpdateDataset",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new("google.cloud.automl.v1.AutoMl", "UpdateDataset"),
);
self.inner.unary(req, path, codec).await
}
/// Deletes a dataset and all of its contents.
/// Returns empty response in the
/// [response][google.longrunning.Operation.response] field when it completes,
/// and `delete_details` in the
/// [metadata][google.longrunning.Operation.metadata] field.
pub async fn delete_dataset(
&mut self,
request: impl tonic::IntoRequest<super::DeleteDatasetRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/DeleteDataset",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new("google.cloud.automl.v1.AutoMl", "DeleteDataset"),
);
self.inner.unary(req, path, codec).await
}
/// Imports data into a dataset.
/// For Tables this method can only be called on an empty Dataset.
///
/// For Tables:
/// * A
/// [schema_inference_version][google.cloud.automl.v1.InputConfig.params]
/// parameter must be explicitly set.
/// Returns an empty response in the
/// [response][google.longrunning.Operation.response] field when it completes.
pub async fn import_data(
&mut self,
request: impl tonic::IntoRequest<super::ImportDataRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/ImportData",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "ImportData"));
self.inner.unary(req, path, codec).await
}
/// Exports dataset's data to the provided output location.
/// Returns an empty response in the
/// [response][google.longrunning.Operation.response] field when it completes.
pub async fn export_data(
&mut self,
request: impl tonic::IntoRequest<super::ExportDataRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/ExportData",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "ExportData"));
self.inner.unary(req, path, codec).await
}
/// Gets an annotation spec.
pub async fn get_annotation_spec(
&mut self,
request: impl tonic::IntoRequest<super::GetAnnotationSpecRequest>,
) -> std::result::Result<tonic::Response<super::AnnotationSpec>, tonic::Status> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/GetAnnotationSpec",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new("google.cloud.automl.v1.AutoMl", "GetAnnotationSpec"),
);
self.inner.unary(req, path, codec).await
}
/// Creates a model.
/// Returns a Model in the [response][google.longrunning.Operation.response]
/// field when it completes.
/// When you create a model, several model evaluations are created for it:
/// a global evaluation, and one evaluation for each annotation spec.
pub async fn create_model(
&mut self,
request: impl tonic::IntoRequest<super::CreateModelRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/CreateModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "CreateModel"));
self.inner.unary(req, path, codec).await
}
/// Gets a model.
pub async fn get_model(
&mut self,
request: impl tonic::IntoRequest<super::GetModelRequest>,
) -> std::result::Result<tonic::Response<super::Model>, tonic::Status> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/GetModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "GetModel"));
self.inner.unary(req, path, codec).await
}
/// Lists models.
pub async fn list_models(
&mut self,
request: impl tonic::IntoRequest<super::ListModelsRequest>,
) -> std::result::Result<
tonic::Response<super::ListModelsResponse>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/ListModels",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "ListModels"));
self.inner.unary(req, path, codec).await
}
/// Deletes a model.
/// Returns `google.protobuf.Empty` in the
/// [response][google.longrunning.Operation.response] field when it completes,
/// and `delete_details` in the
/// [metadata][google.longrunning.Operation.metadata] field.
pub async fn delete_model(
&mut self,
request: impl tonic::IntoRequest<super::DeleteModelRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/DeleteModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "DeleteModel"));
self.inner.unary(req, path, codec).await
}
/// Updates a model.
pub async fn update_model(
&mut self,
request: impl tonic::IntoRequest<super::UpdateModelRequest>,
) -> std::result::Result<tonic::Response<super::Model>, tonic::Status> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/UpdateModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "UpdateModel"));
self.inner.unary(req, path, codec).await
}
/// Deploys a model. If a model is already deployed, deploying it with the
/// same parameters has no effect. Deploying with different parametrs
/// (as e.g. changing
/// [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
/// will reset the deployment state without pausing the model's availability.
///
/// Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage
/// deployment automatically.
///
/// Returns an empty response in the
/// [response][google.longrunning.Operation.response] field when it completes.
pub async fn deploy_model(
&mut self,
request: impl tonic::IntoRequest<super::DeployModelRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/DeployModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "DeployModel"));
self.inner.unary(req, path, codec).await
}
/// Undeploys a model. If the model is not deployed this method has no effect.
///
/// Only applicable for Text Classification, Image Object Detection and Tables;
/// all other domains manage deployment automatically.
///
/// Returns an empty response in the
/// [response][google.longrunning.Operation.response] field when it completes.
pub async fn undeploy_model(
&mut self,
request: impl tonic::IntoRequest<super::UndeployModelRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/UndeployModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new("google.cloud.automl.v1.AutoMl", "UndeployModel"),
);
self.inner.unary(req, path, codec).await
}
/// Exports a trained, "export-able", model to a user specified Google Cloud
/// Storage location. A model is considered export-able if and only if it has
/// an export format defined for it in
/// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
///
/// Returns an empty response in the
/// [response][google.longrunning.Operation.response] field when it completes.
pub async fn export_model(
&mut self,
request: impl tonic::IntoRequest<super::ExportModelRequest>,
) -> std::result::Result<
tonic::Response<super::super::super::super::longrunning::Operation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/ExportModel",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(GrpcMethod::new("google.cloud.automl.v1.AutoMl", "ExportModel"));
self.inner.unary(req, path, codec).await
}
/// Gets a model evaluation.
pub async fn get_model_evaluation(
&mut self,
request: impl tonic::IntoRequest<super::GetModelEvaluationRequest>,
) -> std::result::Result<
tonic::Response<super::ModelEvaluation>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/GetModelEvaluation",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new(
"google.cloud.automl.v1.AutoMl",
"GetModelEvaluation",
),
);
self.inner.unary(req, path, codec).await
}
/// Lists model evaluations.
pub async fn list_model_evaluations(
&mut self,
request: impl tonic::IntoRequest<super::ListModelEvaluationsRequest>,
) -> std::result::Result<
tonic::Response<super::ListModelEvaluationsResponse>,
tonic::Status,
> {
self.inner
.ready()
.await
.map_err(|e| {
tonic::Status::new(
tonic::Code::Unknown,
format!("Service was not ready: {}", e.into()),
)
})?;
let codec = tonic::codec::ProstCodec::default();
let path = http::uri::PathAndQuery::from_static(
"/google.cloud.automl.v1.AutoMl/ListModelEvaluations",
);
let mut req = request.into_request();
req.extensions_mut()
.insert(
GrpcMethod::new(
"google.cloud.automl.v1.AutoMl",
"ListModelEvaluations",
),
);
self.inner.unary(req, path, codec).await
}
}
}