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Synchronize new proto/yaml changes. 

PiperOrigin-RevId: 275954662
This commit is contained in:
Google APIs 2019-10-21 16:55:51 -07:00 committed by Copybara-Service
parent 09c6bd2125
commit 4f34b95cc7
21 changed files with 2320 additions and 47 deletions
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11
google/cloud/automl/automl_v1.yaml
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@ -9,6 +9,7 @@ apis:
types:
- name: google.cloud.automl.v1.OperationMetadata
- name: google.cloud.automl.v1.BatchPredictResult
documentation:
summary: |-
@ -32,6 +33,8 @@ backend:
deadline: 5.0
- selector: google.cloud.automl.v1.PredictionService.Predict
deadline: 60.0
- selector: google.cloud.automl.v1.PredictionService.BatchPredict
deadline: 20.0
- selector: google.cloud.automl.v1.AutoMl.CreateDataset
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.GetDataset
@ -46,6 +49,8 @@ backend:
deadline: 20.0
- selector: google.cloud.automl.v1.AutoMl.ExportData
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.GetAnnotationSpec
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.CreateModel
deadline: 20.0
- selector: google.cloud.automl.v1.AutoMl.GetModel
@ -56,6 +61,12 @@ backend:
deadline: 50.0
- selector: google.cloud.automl.v1.AutoMl.DeleteModel
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.DeployModel
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.UndeployModel
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.ExportModel
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.GetModelEvaluation
deadline: 5.0
- selector: google.cloud.automl.v1.AutoMl.ListModelEvaluations

31
google/cloud/automl/v1/annotation_payload.proto
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@ -17,12 +17,16 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/cloud/automl/v1/classification.proto";
import "google/cloud/automl/v1/detection.proto";
import "google/cloud/automl/v1/text_extraction.proto";
import "google/cloud/automl/v1/text_sentiment.proto";
import "google/cloud/automl/v1/translation.proto";
import "google/protobuf/any.proto";
import "google/api/annotations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
@ -35,5 +39,30 @@ message AnnotationPayload {
oneof detail {
// Annotation details for translation.
TranslationAnnotation translation = 2;
// Annotation details for content or image classification.
ClassificationAnnotation classification = 3;
// Annotation details for image object detection.
ImageObjectDetectionAnnotation image_object_detection = 4;
// Annotation details for text extraction.
TextExtractionAnnotation text_extraction = 6;
// Annotation details for text sentiment.
TextSentimentAnnotation text_sentiment = 7;
}
// 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.
string annotation_spec_id = 1;
// Output only. The value of
// [display_name][google.cloud.automl.v1p1beta.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.
string display_name = 5;
}

46
google/cloud/automl/v1/annotation_spec.proto Normal file
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@ -0,0 +1,46 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// A definition of an annotation spec.
message AnnotationSpec {
// Output only. Resource name of the annotation spec.
// Form:
//
// 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
string name = 1;
// 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_]+`.
// (_), and ASCII digits 0-9.
string display_name = 2;
// Output only. The number of examples in the parent dataset
// labeled by the annotation spec.
int32 example_count = 9;
}

109
google/cloud/automl/v1/automl_gapic.yaml
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@ -36,6 +36,8 @@ interfaces:
entity_name: location
- name_pattern: projects/{project}/locations/{location}/datasets/{dataset}
entity_name: dataset
- name_pattern: projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}
entity_name: annotation_spec
- name_pattern: projects/{project}/locations/{location}/models/{model}
entity_name: model
- name_pattern: projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}
@ -237,6 +239,18 @@ interfaces:
poll_delay_multiplier: 1.5
max_poll_delay_millis: 5000
total_poll_timeout_millis: 300000
- name: GetAnnotationSpec
flattening:
groups:
- parameters:
- name
required_fields:
- name
retry_codes_name: idempotent
retry_params_name: default
field_name_patterns:
name: annotation_spec
timeout_millis: 5000
- name: CreateModel
flattening:
groups:
@ -327,6 +341,62 @@ interfaces:
poll_delay_multiplier: 1.5
max_poll_delay_millis: 5000
total_poll_timeout_millis: 300000
- name: DeployModel
flattening:
groups:
- parameters:
- name
required_fields:
- name
retry_codes_name: non_idempotent
retry_params_name: default
field_name_patterns:
name: model
resource_name_treatment: STATIC_TYPES
timeout_millis: 5000
long_running:
return_type: google.protobuf.Empty
metadata_type: google.cloud.automl.v1.OperationMetadata
initial_poll_delay_millis: 500
poll_delay_multiplier: 1.5
max_poll_delay_millis: 5000
total_poll_timeout_millis: 3600000
- name: UndeployModel
flattening:
groups:
- parameters:
- name
required_fields:
- name
retry_codes_name: non_idempotent
retry_params_name: default
field_name_patterns:
name: model
resource_name_treatment: STATIC_TYPES
timeout_millis: 5000
long_running:
return_type: google.protobuf.Empty
metadata_type: google.cloud.automl.v1.OperationMetadata
initial_poll_delay_millis: 500
poll_delay_multiplier: 1.5
max_poll_delay_millis: 5000
total_poll_timeout_millis: 3600000
- name: ExportModel
required_fields:
- name
- output_config
retry_codes_name: non_idempotent
retry_params_name: default
field_name_patterns:
name: model
long_running:
return_type: google.protobuf.Empty
metadata_type: google.cloud.automl.v1.OperationMetadata
initial_poll_delay_millis: 500
poll_delay_multiplier: 1.5
max_poll_delay_millis: 5000
total_poll_timeout_millis: 300000
timeout_millis: 5000
- name: GetModelEvaluation
flattening:
groups:
@ -455,6 +525,30 @@ interfaces:
name: model
resource_name_treatment: STATIC_TYPES
timeout_millis: 60000
- name: BatchPredict
required_fields:
- name
- input_config
- output_config
flattening:
groups:
- parameters:
- name
- input_config
- output_config
- params
retry_codes_name: non_idempotent
retry_params_name: default
field_name_patterns:
name: model
long_running:
return_type: google.cloud.automl.v1.BatchPredictResult
metadata_type: google.cloud.automl.v1.OperationMetadata
initial_poll_delay_millis: 500
poll_delay_multiplier: 1.5
max_poll_delay_millis: 5000
total_poll_timeout_millis: 86400000
timeout_millis: 20000
resource_name_generation:
- message_name: CreateDatasetRequest
field_entity_map:
@ -477,6 +571,9 @@ resource_name_generation:
- message_name: ExportDataRequest
field_entity_map:
name: dataset
- message_name: GetAnnotationSpecRequest
field_entity_map:
name: annotation_spec
- message_name: CreateModelRequest
field_entity_map:
parent: location
@ -492,6 +589,15 @@ resource_name_generation:
- message_name: DeleteModelRequest
field_entity_map:
name: model
- message_name: DeployModelRequest
field_entity_map:
name: model
- message_name: UndeployModelRequest
field_entity_map:
name: model
- message_name: ExportModelRequest
field_entity_map:
name: model
- message_name: GetModelEvaluationRequest
field_entity_map:
name: model_evaluation
@ -501,3 +607,6 @@ resource_name_generation:
- message_name: PredictRequest
field_entity_map:
name: model
- message_name: BatchPredictRequest
field_entity_map:
name: model

172
google/cloud/automl/v1/classification.proto Normal file
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@ -0,0 +1,172 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "ClassificationProto";
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Type of the classification problem.
enum ClassificationType {
// An un-set value of this enum.
CLASSIFICATION_TYPE_UNSPECIFIED = 0;
// At most one label is allowed per example.
MULTICLASS = 1;
// Multiple labels are allowed for one example.
MULTILABEL = 2;
}
// Contains annotation details specific to classification.
message 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.
float score = 1;
}
// Model evaluation metrics for classification problems.
message ClassificationEvaluationMetrics {
// Metrics for a single confidence threshold.
message ConfidenceMetricsEntry {
// Output only. Metrics are computed with an assumption that the model
// never returns predictions with score lower than this value.
float confidence_threshold = 1;
// 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.
int32 position_threshold = 14;
// Output only. Recall (True Positive Rate) for the given confidence
// threshold.
float recall = 2;
// Output only. Precision for the given confidence threshold.
float precision = 3;
// Output only. False Positive Rate for the given confidence threshold.
float false_positive_rate = 8;
// Output only. The harmonic mean of recall and precision.
float f1_score = 4;
// 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.
float recall_at1 = 5;
// Output only. The precision when only considering the label that has the
// highest prediction score and not below the confidence threshold for each
// example.
float precision_at1 = 6;
// 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.
float false_positive_rate_at1 = 9;
// 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].
float f1_score_at1 = 7;
// Output only. The number of model created labels that match a ground truth
// label.
int64 true_positive_count = 10;
// Output only. The number of model created labels that do not match a
// ground truth label.
int64 false_positive_count = 11;
// Output only. The number of ground truth labels that are not matched
// by a model created label.
int64 false_negative_count = 12;
// 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.
int64 true_negative_count = 13;
}
// Confusion matrix of the model running the classification.
message ConfusionMatrix {
// Output only. A row in the confusion matrix.
message 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.
repeated int32 example_count = 1;
}
// Output only. IDs of the annotation specs used in the confusion matrix.
repeated string annotation_spec_id = 1;
// Output only. Display name of the annotation specs used in the confusion
// matrix, as they were at the moment of the evaluation.
repeated string display_name = 3;
// 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.
repeated Row row = 2;
}
// Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
// for the overall evaluation.
float au_prc = 1;
// Output only. The Area Under Receiver Operating Characteristic curve metric.
// Micro-averaged for the overall evaluation.
float au_roc = 6;
// Output only. The Log Loss metric.
float log_loss = 7;
// 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.
repeated ConfidenceMetricsEntry confidence_metrics_entry = 3;
// Output only. Confusion matrix of the evaluation.
// Only set for MULTICLASS classification problems where number
// of labels is no more than 10.
// Only set for model level evaluation, not for evaluation per label.
ConfusionMatrix confusion_matrix = 4;
// Output only. The annotation spec ids used for this evaluation.
repeated string annotation_spec_id = 5;
}

145
google/cloud/automl/v1/data_items.proto
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@ -17,19 +17,42 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/cloud/automl/v1/geometry.proto";
import "google/cloud/automl/v1/io.proto";
import "google/cloud/automl/v1/text_segment.proto";
import "google/protobuf/any.proto";
import "google/protobuf/duration.proto";
import "google/protobuf/struct.proto";
import "google/api/annotations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// A representation of an image.
// Only images up to 30MB in size are supported.
message Image {
// Input only. The data representing the image.
// For Predict calls [image_bytes][google.cloud.automl.v1.Image.image_bytes] must be set, as other options are not
// currently supported by prediction API. You can read the contents of an
// uploaded image by using the [content_uri][google.cloud.automl.v1.Image.content_uri] field.
oneof 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.
bytes image_bytes = 1;
// An input config specifying the content of the image.
ImageInputConfig image_input_config = 7;
}
// Output only. HTTP URI to the thumbnail image.
string thumbnail_uri = 4;
}
// A representation of a text snippet.
message TextSnippet {
// Required. The content of the text snippet as a string. Up to 250000
@ -45,11 +68,131 @@ message TextSnippet {
string content_uri = 4;
}
// Message that describes dimension of a document.
message DocumentDimensions {
// Unit of the document dimension.
enum DocumentDimensionUnit {
// Should not be used.
DOCUMENT_DIMENSION_UNIT_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;
}
// Unit of the dimension.
DocumentDimensionUnit unit = 1;
// Width value of the document, works together with the unit.
float width = 2;
// Height value of the document, works together with the unit.
float height = 3;
}
// A structured text document e.g. a PDF.
message Document {
// Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.
message Layout {
// The type of TextSegment in the context of the original document.
enum TextSegmentType {
// Should not be used.
TEXT_SEGMENT_TYPE_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.
FORM_FIELD = 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.
FORM_FIELD_NAME = 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.
FORM_FIELD_CONTENTS = 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.
TABLE_HEADER = 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.
TABLE_ROW = 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.
TABLE_CELL = 9;
}
// Text Segment that represents a segment in
// [document_text][google.cloud.automl.v1p1beta.Document.document_text].
TextSegment text_segment = 1;
// Page number of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the original document, starts
// from 1.
int32 page_number = 2;
// The position of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the page.
// Contains exactly 4
//
// [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices]
// and they are connected by edges in the order provided, which will
// represent a rectangle parallel to the frame. The
// [NormalizedVertex-s][google.cloud.automl.v1p1beta.NormalizedVertex] are
// relative to the page.
// Coordinates are based on top-left as point (0,0).
BoundingPoly bounding_poly = 3;
// The type of the [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in document.
TextSegmentType text_segment_type = 4;
}
// An input config specifying the content of the document.
DocumentInputConfig input_config = 1;
// The plain text version of this document.
TextSnippet document_text = 2;
// Describes the layout of the document.
// Sorted by [page_number][].
repeated Layout layout = 3;
// The dimensions of the page in the document.
DocumentDimensions document_dimensions = 4;
// Number of pages in the document.
int32 page_count = 5;
}
// Example data used for training or prediction.
message ExamplePayload {
// Required. Input only. The example data.
oneof payload {
// Example image.
Image image = 1;
// Example text.
TextSnippet text_snippet = 2;
// Example document.
Document document = 4;
}
}

20
google/cloud/automl/v1/dataset.proto
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@ -17,12 +17,15 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/resource.proto";
import "google/cloud/automl/v1/image.proto";
import "google/cloud/automl/v1/text.proto";
import "google/cloud/automl/v1/translation.proto";
import "google/protobuf/timestamp.proto";
import "google/api/annotations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
@ -36,6 +39,21 @@ message Dataset {
oneof dataset_metadata {
// Metadata for a dataset used for translation.
TranslationDatasetMetadata translation_dataset_metadata = 23;
// Metadata for a dataset used for image classification.
ImageClassificationDatasetMetadata image_classification_dataset_metadata = 24;
// Metadata for a dataset used for text classification.
TextClassificationDatasetMetadata text_classification_dataset_metadata = 25;
// Metadata for a dataset used for image object detection.
ImageObjectDetectionDatasetMetadata image_object_detection_dataset_metadata = 26;
// Metadata for a dataset used for text extraction.
TextExtractionDatasetMetadata text_extraction_dataset_metadata = 28;
// Metadata for a dataset used for text sentiment.
TextSentimentDatasetMetadata text_sentiment_dataset_metadata = 30;
}
// Output only. The resource name of the dataset.

88
google/cloud/automl/v1/detection.proto Normal file
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@ -0,0 +1,88 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/cloud/automl/v1/geometry.proto";
import "google/protobuf/duration.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Annotation details for image object detection.
message ImageObjectDetectionAnnotation {
// Output only. The rectangle representing the object location.
BoundingPoly bounding_box = 1;
// Output only. The confidence that this annotation is positive for the parent
// example, value in [0, 1], higher means higher positivity confidence.
float score = 2;
}
// Bounding box matching model metrics for a single intersection-over-union
// threshold and multiple label match confidence thresholds.
message BoundingBoxMetricsEntry {
// Metrics for a single confidence threshold.
message ConfidenceMetricsEntry {
// Output only. The confidence threshold value used to compute the metrics.
float confidence_threshold = 1;
// Output only. Recall under the given confidence threshold.
float recall = 2;
// Output only. Precision under the given confidence threshold.
float precision = 3;
// Output only. The harmonic mean of recall and precision.
float f1_score = 4;
}
// Output only. The intersection-over-union threshold value used to compute
// this metrics entry.
float iou_threshold = 1;
// Output only. The mean average precision, most often close to au_prc.
float mean_average_precision = 2;
// 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.
repeated ConfidenceMetricsEntry confidence_metrics_entries = 3;
}
// Model evaluation metrics for image object detection problems.
// Evaluates prediction quality of labeled bounding boxes.
message ImageObjectDetectionEvaluationMetrics {
// Output only. The total number of bounding boxes (i.e. summed over all
// images) the ground truth used to create this evaluation had.
int32 evaluated_bounding_box_count = 1;
// 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.
repeated BoundingBoxMetricsEntry bounding_box_metrics_entries = 2;
// Output only. The single metric for bounding boxes evaluation:
// the mean_average_precision averaged over all bounding_box_metrics_entries.
float bounding_box_mean_average_precision = 3;
}

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// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// 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.
message NormalizedVertex {
// Required. Horizontal coordinate.
float x = 1;
// Required. Vertical coordinate.
float y = 2;
}
// 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.
message BoundingPoly {
// Output only . The bounding polygon normalized vertices.
repeated NormalizedVertex normalized_vertices = 2;
}

194
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// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/api/resource.proto";
import "google/cloud/automl/v1/annotation_spec.proto";
import "google/cloud/automl/v1/classification.proto";
import "google/protobuf/timestamp.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "ImageProto";
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Dataset metadata that is specific to image classification.
message ImageClassificationDatasetMetadata {
// Required. Type of the classification problem.
ClassificationType classification_type = 1;
}
// Dataset metadata specific to image object detection.
message ImageObjectDetectionDatasetMetadata {}
// Model metadata for image classification.
message 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`.
string base_model_id = 1;
// 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.
int64 train_budget_milli_node_hours = 16;
// 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.
int64 train_cost_milli_node_hours = 17;
// Output only. The reason that this create model operation stopped,
// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5;
// 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.
string model_type = 7;
// Output only. An approximate number of online prediction QPS that can
// be supported by this model per each node on which it is deployed.
double node_qps = 13;
// 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.
int64 node_count = 14;
}
// Model metadata specific to image object detection.
message 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.
string model_type = 1;
// 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.
int64 node_count = 3;
// Output only. An approximate number of online prediction QPS that can
// be supported by this model per each node on which it is deployed.
double node_qps = 4;
// Output only. The reason that this create model operation stopped,
// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
string stop_reason = 5;
// 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.
int64 train_budget_milli_node_hours = 6;
// 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.
int64 train_cost_milli_node_hours = 7;
}
// Model deployment metadata specific to Image Classification.
message 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.
int64 node_count = 1;
}
// Model deployment metadata specific to Image Object Detection.
message 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.
int64 node_count = 1;
}

835
google/cloud/automl/v1/io.proto
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@ -18,28 +18,451 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/api/field_behavior.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Input configuration for ImportData Action.
// 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.v1beta1.InputConfig.gcs_source]
// [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
// (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
// provided with the same `ML_USE` and `GCS_FILE_PATH`, if it is not, then
// these values are nondeterministically selected from the given ones.
//
// Errors:
// 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 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 PDF document.
// Multiple JSON documents can be separated using line breaks (\n).
//
// For example:
//
// {
// "document": {
// "input_config": {
// "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
// }
// }
// }
// }\n
// {
// "document": {
// "input_config": {
// "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
// }
// }
// }
// }
//
// **In-line JSONL files with PDF layout information**
//
// **Note:** You can only annotate PDF files using the UI. The format described
// below applies to annotated PDF files exported using the UI or `exportData`.
//
// In-line .JSONL files for PDF documents contain, per line, a JSON document
// that wraps a `document` field that provides the textual content of the PDF
// 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, .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, .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>
//
//
// **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.
//
// `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.
//
// `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
@ -50,8 +473,9 @@ message InputConfig {
// The source of the input.
oneof source {
// The Google Cloud Storage location for the input content.
// In ImportData, the gcs_source points to a csv with structure described in
// the comment.
// For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData],
// `gcs_source` points to a CSV file with a structure described in
// [InputConfig][google.cloud.automl.v1.InputConfig].
GcsSource gcs_source = 1;
}
@ -61,40 +485,399 @@ message InputConfig {
map<string, string> params = 2;
}
// * For Translation:
// 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 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
// Text files can be no larger than 10MB in size.
//
// Sample rows:
//
// gs://folder/text1.txt
// gs://folder/text2.pdf
//
// </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
// Text files can be no larger than 128kB in size.
//
// Sample rows:
//
// gs://folder/text1.txt
// gs://folder/text2.pdf
//
// </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. Only PDF documents are
// currently supported, and each PDF document cannot exceed 2MB in size.
//
// 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.pdf" ]
// }
// }
// }
// }
// </section>
// </div>
//
// **Input field definitions:**
//
// `GCS_FILE_PATH`
// : The path to a file on Google Cloud Storage. For example,
// "gs://folder/video.avi".
//
// **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.
message BatchPredictInputConfig {
// The source of the input.
oneof source {
// Required. The Google Cloud Storage location for the input content.
GcsSource gcs_source = 1 [(google.api.field_behavior) = REQUIRED];
}
}
// Input configuration of an [Document][google.cloud.automl.v1.Image].
message ImageInputConfig {
// The Google Cloud Storage location of the document file. Only a single path
// should be given.
GcsSource gcs_source = 1;
}
// Input configuration of a [Document][google.cloud.automl.v1.Document].
message DocumentInputConfig {
// The Google Cloud Storage location of the document file. Only a single path
// should be given.
//
// Max supported size: 512MB.
//
// Supported extensions: .PDF.
GcsSource gcs_source = 1;
}
// Output configuration for ExportData.
//
// As destination the
// [gcs_destination][google.cloud.automl.v1.OutputConfig.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 "export_data-<dataset-display-name>-<timestamp-of-export-call>",
// where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format.
// Only ground truth annotations are exported (not approved annotations are
// not exported).
//
// The outputs correspond to how the data was imported, and may be used as
// input to import data. The output formats are represented as EBNF with literal
// commas and same non-terminal symbols definitions are these in import data's
// [InputConfig][google.cloud.automl.v1.InputConfig]:
//
// * For Image Classification:
// CSV file(s) `image_classification_1.csv`,
// `image_classification_2.csv`,...,`image_classification_N.csv`with
// each line in format:
// ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
// where GCS_FILE_PATHs point at the original, source locations of the
// imported images.
// For MULTICLASS classification type, there can be at most one LABEL
// per example.
//
// * For Image Object Detection:
// CSV file(s) `image_object_detection_1.csv`,
// `image_object_detection_2.csv`,...,`image_object_detection_N.csv`
// with each line in format:
// ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)
// where GCS_FILE_PATHs point at the original, source locations of the
// imported images.
//
// * For Text Classification:
// In the created directory CSV file(s) `text_classification_1.csv`,
// `text_classification_2.csv`, ...,`text_classification_N.csv` will be
// created where N depends on the total number of examples exported.
// Each line in the CSV is of the format:
// ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
// where GCS_FILE_PATHs point at the exported .txt files containing
// the text content of the imported example. For MULTICLASS
// classification type, there will be at most one LABEL per example.
//
// * For Text Sentiment:
// In the created directory CSV file(s) `text_sentiment_1.csv`,
// `text_sentiment_2.csv`, ...,`text_sentiment_N.csv` will be
// created where N depends on the total number of examples exported.
// Each line in the CSV is of the format:
// ML_USE,GCS_FILE_PATH,SENTIMENT
// where GCS_FILE_PATHs point at the exported .txt files containing
// the text content of the imported example.
//
// * For Text Extraction:
// CSV file `text_extraction.csv`, with each line in format:
// ML_USE,GCS_FILE_PATH
// GCS_FILE_PATH leads to a .JSONL (i.e. JSON Lines) file which
// contains, per line, a proto that wraps a TextSnippet proto (in json
// representation) followed by AnnotationPayload protos (called
// annotations). If initially documents had been imported, the JSONL
// will point at the original, source locations of the imported
// documents.
//
// * 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)
//
// `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.
message OutputConfig {
// Required. The destination of the output.
// The destination of the output.
oneof destination {
// 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:
// Required. The Google Cloud Storage location where the output is to be
// written to. For Image Object Detection, Text Extraction in the given
// directory a new directory will be created with name:
// export_data-<dataset-display-name>-<timestamp-of-export-call> where
// timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export
// output will be written into that directory.
GcsDestination gcs_destination = 1;
GcsDestination gcs_destination = 1 [(google.api.field_behavior) = REQUIRED];
}
}
// 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 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 (or pdf) file 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 (or pdf) file
// will be listed only once with all its annotations, and its
// annotations will never be split across files.
//
// If prediction for any text (or pdf) file failed (partially or
// completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
// `errors_N.jsonl` files will be created (N depends on total number of
// failed predictions). These files will have a JSON representation of a
// proto that wraps input text (or pdf) 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 (or pdf) file 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 (or pdf) file
// will be listed only once with all its annotations, and its
// annotations will never be split across files.
//
// If prediction for any text (or pdf) file failed (partially or
// completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
// `errors_N.jsonl` files will be created (N depends on total number of
// failed predictions). These files will have a JSON representation of a
// proto that wraps input text (or pdf) 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`.
message BatchPredictOutputConfig {
// The destination of the output.
oneof destination {
// Required. The Google Cloud Storage location of the directory where the
// output is to be written to.
GcsDestination gcs_destination = 1 [(google.api.field_behavior) = REQUIRED];
}
}
// Output configuration for ModelExport Action.
message ModelExportOutputConfig {
// The destination of the output.
oneof 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.
GcsDestination gcs_destination = 1 [(google.api.field_behavior) = REQUIRED];
}
// 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".
//
// * 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.x`
// * core_ml - Used for iOS mobile devices.
string model_format = 4;
// 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.
map<string, string> params = 2;
}
// The Google Cloud Storage location for the input content.
message GcsSource {
// Required. Google Cloud Storage URIs to input files, up to 2000 characters
// long. Accepted forms:
// 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
repeated string input_uris = 1;
repeated string input_uris = 1 [(google.api.field_behavior) = REQUIRED];
}
// The Google Cloud Storage location where the output is to be written to.
@ -105,5 +888,5 @@ message GcsDestination {
// * Prefix path: gs://bucket/directory
// The requesting user must have write permission to the bucket.
// The directory is created if it doesn't exist.
string output_uri_prefix = 1;
string output_uri_prefix = 1 [(google.api.field_behavior) = REQUIRED];
}

29
google/cloud/automl/v1/model.proto
View File

@ -17,12 +17,15 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/resource.proto";
import "google/cloud/automl/v1/image.proto";
import "google/cloud/automl/v1/text.proto";
import "google/cloud/automl/v1/translation.proto";
import "google/protobuf/timestamp.proto";
import "google/api/annotations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
@ -30,6 +33,11 @@ option ruby_package = "Google::Cloud::AutoML::V1";
// API proto representing a trained machine learning model.
message Model {
option (google.api.resource) = {
type: "automl.googleapis.com/Model"
pattern: "projects/{project_id}/locations/{location_id}/models/{model_id}"
};
// Deployment state of the model.
enum DeploymentState {
// Should not be used, an un-set enum has this value by default.
@ -48,6 +56,21 @@ message Model {
oneof model_metadata {
// Metadata for translation models.
TranslationModelMetadata translation_model_metadata = 15;
// Metadata for image classification models.
ImageClassificationModelMetadata image_classification_model_metadata = 13;
// Metadata for text classification models.
TextClassificationModelMetadata text_classification_model_metadata = 14;
// Metadata for image object detection models.
ImageObjectDetectionModelMetadata image_object_detection_model_metadata = 20;
// Metadata for text extraction models.
TextExtractionModelMetadata text_extraction_model_metadata = 19;
// Metadata for text sentiment models.
TextSentimentModelMetadata text_sentiment_model_metadata = 22;
}
// Output only. Resource name of the model.
@ -74,6 +97,10 @@ message Model {
// prediction requests after it gets deployed.
DeploymentState deployment_state = 8;
// Used to perform a consistent read-modify-write updates. If not set, a blind
// "overwrite" update happens.
string etag = 10;
// Optional. The labels with user-defined metadata to organize your model.
//
// Label keys and values can be no longer than 64 characters

36
google/cloud/automl/v1/model_evaluation.proto
View File

@ -17,12 +17,17 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/api/resource.proto";
import "google/cloud/automl/v1/classification.proto";
import "google/cloud/automl/v1/detection.proto";
import "google/cloud/automl/v1/text_extraction.proto";
import "google/cloud/automl/v1/text_sentiment.proto";
import "google/cloud/automl/v1/translation.proto";
import "google/protobuf/timestamp.proto";
import "google/api/annotations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
@ -32,8 +37,21 @@ option ruby_package = "Google::Cloud::AutoML::V1";
message ModelEvaluation {
// Output only. Problem type specific evaluation metrics.
oneof metrics {
// Model evaluation metrics for image, text classification.
ClassificationEvaluationMetrics classification_evaluation_metrics = 8;
// Model evaluation metrics for translation.
TranslationEvaluationMetrics translation_evaluation_metrics = 9;
// Model evaluation metrics for image object detection.
ImageObjectDetectionEvaluationMetrics
image_object_detection_evaluation_metrics = 12;
// Evaluation metrics for text sentiment models.
TextSentimentEvaluationMetrics text_sentiment_evaluation_metrics = 11;
// Evaluation metrics for text extraction models.
TextExtractionEvaluationMetrics text_extraction_evaluation_metrics = 13;
}
// Output only. Resource name of the model evaluation.
@ -42,10 +60,18 @@ message ModelEvaluation {
// `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
string name = 1;
// Output only. The ID of the annotation spec that the model evaluation applies to. The
// The ID is empty for the overall model evaluation.
// Output only. The ID of the annotation spec that the model evaluation
// applies to. The The ID is empty for the overall model evaluation.
string annotation_spec_id = 2;
// 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.
string display_name = 15;
// Output only. Timestamp when this model evaluation was created.
google.protobuf.Timestamp create_time = 5;
@ -57,6 +83,6 @@ message ModelEvaluation {
// Otherwise, this is the count of examples that according to the ground
// truth were annotated by the
//
// [annotation_spec_id][google.cloud.automl.v1beta1.ModelEvaluation.annotation_spec_id].
// [annotation_spec_id][google.cloud.automl.v1.ModelEvaluation.annotation_spec_id].
int32 evaluated_example_count = 6;
}

103
google/cloud/automl/v1/operations.proto
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@ -17,16 +17,19 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/cloud/automl/v1/dataset.proto";
import "google/cloud/automl/v1/io.proto";
import "google/cloud/automl/v1/model.proto";
import "google/cloud/automl/v1/model_evaluation.proto";
import "google/cloud/automl/v1/prediction_service.proto";
import "google/cloud/automl/v1/service.proto";
import "google/protobuf/empty.proto";
import "google/protobuf/timestamp.proto";
import "google/rpc/status.proto";
import "google/api/annotations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
@ -40,8 +43,29 @@ message OperationMetadata {
// Details of a Delete operation.
DeleteOperationMetadata delete_details = 8;
// Details of a DeployModel operation.
DeployModelOperationMetadata deploy_model_details = 24;
// Details of an UndeployModel operation.
UndeployModelOperationMetadata undeploy_model_details = 25;
// Details of CreateModel operation.
CreateModelOperationMetadata create_model_details = 10;
// Details of CreateDataset operation.
CreateDatasetOperationMetadata create_dataset_details = 30;
// Details of ImportData operation.
ImportDataOperationMetadata import_data_details = 15;
// Details of BatchPredict operation.
BatchPredictOperationMetadata batch_predict_details = 16;
// Details of ExportData operation.
ExportDataOperationMetadata export_data_details = 21;
// Details of ExportModel operation.
ExportModelOperationMetadata export_model_details = 22;
}
// Output only. Progress of operation. Range: [0, 100].
@ -66,7 +90,84 @@ message DeleteOperationMetadata {
}
// Details of DeployModel operation.
message DeployModelOperationMetadata {
}
// Details of UndeployModel operation.
message UndeployModelOperationMetadata {
}
// Details of CreateDataset operation.
message CreateDatasetOperationMetadata {
}
// Details of CreateModel operation.
message CreateModelOperationMetadata {
}
// Details of ImportData operation.
message ImportDataOperationMetadata {
}
// Details of ExportData operation.
message ExportDataOperationMetadata {
// Further describes this export data's output.
// Supplements
// [OutputConfig][google.cloud.automl.v1.OutputConfig].
message ExportDataOutputInfo {
// The output location to which the exported data is written.
oneof output_location {
// The full path of the Google Cloud Storage directory created, into which
// the exported data is written.
string gcs_output_directory = 1;
}
}
// Output only. Information further describing this export data's output.
ExportDataOutputInfo output_info = 1;
}
// Details of BatchPredict operation.
message BatchPredictOperationMetadata {
// Further describes this batch predict's output.
// Supplements
//
// [BatchPredictOutputConfig][google.cloud.automl.v1.BatchPredictOutputConfig].
message BatchPredictOutputInfo {
// The output location into which prediction output is written.
oneof output_location {
// The full path of the Google Cloud Storage directory created, into which
// the prediction output is written.
string gcs_output_directory = 1;
}
}
// Output only. The input config that was given upon starting this
// batch predict operation.
BatchPredictInputConfig input_config = 1;
// Output only. Information further describing this batch predict's output.
BatchPredictOutputInfo output_info = 2;
}
// Details of ExportModel operation.
message ExportModelOperationMetadata {
// Further describes the output of model export.
// Supplements
// [ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
message ExportModelOutputInfo {
// The full path of the Google Cloud Storage directory created, into which
// the model will be exported.
string gcs_output_directory = 1;
}
// Output only. Information further describing the output of this model
// export.
ExportModelOutputInfo output_info = 2;
}

128
google/cloud/automl/v1/prediction_service.proto
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@ -19,14 +19,14 @@ package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/api/client.proto";
import "google/api/resource.proto";
import "google/cloud/automl/v1/annotation_payload.proto";
import "google/cloud/automl/v1/data_items.proto";
import "google/cloud/automl/v1/io.proto";
import "google/cloud/automl/v1/operations.proto";
import "google/longrunning/operations.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "PredictionServiceProto";
option java_package = "com.google.cloud.automl.v1";
@ -45,14 +45,43 @@ service PredictionService {
// Perform an online prediction. The prediction result will be directly
// returned in the response.
// Available for following ML problems, and their expected request payloads:
// * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes
// up to 30MB.
// * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes
// up to 30MB.
// * Text Classification - TextSnippet, content up to 60,000 characters,
// UTF-8 encoded.
// * Text Extraction - TextSnippet, content up to 30,000 characters,
// UTF-8 NFC encoded.
// * Translation - TextSnippet, content up to 25,000 characters, UTF-8
// encoded.
// * Text Sentiment - TextSnippet, content up 500 characters, UTF-8
// encoded.
rpc Predict(PredictRequest) returns (PredictResponse) {
option (google.api.http) = {
post: "/v1/{name=projects/*/locations/*/models/*}:predict"
body: "*"
};
}
// 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 problems:
// * Image Classification
// * Image Object Detection
// * Text Extraction
rpc BatchPredict(BatchPredictRequest) returns (google.longrunning.Operation) {
option (google.api.http) = {
post: "/v1/{name=projects/*/locations/*/models/*}:batchPredict"
body: "*"
};
}
}
// Request message for
@ -67,6 +96,20 @@ message PredictRequest {
// Additional domain-specific parameters, any string must be up to 25000
// characters long.
//
// * For Image 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.
//
// * For Image 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) No more than this number of bounding
// boxes will be returned in the response. Default is 100, the
// requested value may be limited by server.
map<string, string> params = 3;
}
@ -77,6 +120,87 @@ message PredictResponse {
// Translation and Text Sentiment will return precisely one payload.
repeated AnnotationPayload payload = 1;
// The preprocessed example that AutoML actually makes prediction on.
// Empty if AutoML does not preprocess the input example.
// * For Text Extraction:
// If the input is a .pdf file, the OCR'ed text will be provided in
// [document_text][google.cloud.automl.v1p1beta.Document.document_text].
//
// * For Text Classification:
// If the input is a .pdf file, the OCR'ed trucated text will be provided in
// [document_text][google.cloud.automl.v1p1beta.Document.document_text].
//
// * For Text Sentiment:
// If the input is a .pdf file, the OCR'ed trucated text will be provided in
// [document_text][google.cloud.automl.v1p1beta.Document.document_text].
ExamplePayload preprocessed_input = 3;
// Additional domain-specific prediction response metadata.
//
// * For Image Object Detection:
// `max_bounding_box_count` - (int64) At most that many bounding boxes per
// image could have been returned.
//
// * For Text Sentiment:
// `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 will be also positive (though
// the least).
// The sentiment_score shouldn't be confused with "score" or "magnitude"
// from the previous Natural Language Sentiment Analysis API.
map<string, string> metadata = 2;
}
// Request message for
// [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
message BatchPredictRequest {
// Name of the model requested to serve the batch prediction.
string name = 1;
// Required. The input configuration for batch prediction.
BatchPredictInputConfig input_config = 3;
// Required. The Configuration specifying where output predictions should
// be written.
BatchPredictOutputConfig output_config = 4;
// Additional domain-specific parameters for the predictions, any string must
// be up to 25000 characters long.
//
// * For Text 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.
//
// * For Image 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.
//
// * For Image 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) No more than this number of bounding
// boxes will be produced per image. Default is 100, the
// requested value may be limited by server.
map<string, string> params = 5;
}
// 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].
message BatchPredictResult {
// Additional domain-specific prediction response metadata.
//
// * For Image Object Detection:
// `max_bounding_box_count` - (int64) At most that many bounding boxes per
// image could have been returned.
map<string, string> metadata = 1;
}

111
google/cloud/automl/v1/service.proto
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@ -19,17 +19,19 @@ package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/api/client.proto";
import "google/api/resource.proto";
import "google/cloud/automl/v1/annotation_payload.proto";
import "google/cloud/automl/v1/annotation_spec.proto";
import "google/cloud/automl/v1/dataset.proto";
import "google/cloud/automl/v1/image.proto";
import "google/cloud/automl/v1/io.proto";
import "google/cloud/automl/v1/model.proto";
import "google/cloud/automl/v1/model_evaluation.proto";
import "google/cloud/automl/v1/operations.proto";
import "google/longrunning/operations.proto";
import "google/protobuf/field_mask.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "AutoMlProto";
option java_package = "com.google.cloud.automl.v1";
@ -116,6 +118,14 @@ service AutoMl {
};
}
// Gets an annotation spec.
rpc GetAnnotationSpec(GetAnnotationSpecRequest)
returns (google.cloud.automl.v1.AnnotationSpec) {
option (google.api.http) = {
get: "/v1/{name=projects/*/locations/*/datasets/*/annotationSpecs/*}"
};
}
// Creates a model.
// Returns a Model in the [response][google.longrunning.Operation.response]
// field when it completes.
@ -161,6 +171,54 @@ service AutoMl {
};
}
// 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; all other
// domains manage deployment automatically.
//
// Returns an empty response in the
// [response][google.longrunning.Operation.response] field when it completes.
rpc DeployModel(DeployModelRequest) returns (google.longrunning.Operation) {
option (google.api.http) = {
post: "/v1/{name=projects/*/locations/*/models/*}:deploy"
body: "*"
};
}
// Undeploys a model. If the model is not deployed this method has no effect.
//
// Only applicable for Text Classification, Image Object Detection;
// all other domains manage deployment automatically.
//
// Returns an empty response in the
// [response][google.longrunning.Operation.response] field when it completes.
rpc UndeployModel(UndeployModelRequest)
returns (google.longrunning.Operation) {
option (google.api.http) = {
post: "/v1/{name=projects/*/locations/*/models/*}:undeploy"
body: "*"
};
}
// 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.
rpc ExportModel(ExportModelRequest) returns (google.longrunning.Operation) {
option (google.api.http) = {
post: "/v1/{name=projects/*/locations/*/models/*}:export"
body: "*"
};
}
// Gets a model evaluation.
rpc GetModelEvaluation(GetModelEvaluationRequest) returns (ModelEvaluation) {
option (google.api.http) = {
@ -203,8 +261,8 @@ message ListDatasetsRequest {
// 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:
// image_classification_dataset_metadata:*). Some examples of
// using the filter are:
//
// * `translation_dataset_metadata:*` --> The dataset has
// translation_dataset_metadata.
@ -274,6 +332,13 @@ message ExportDataRequest {
OutputConfig output_config = 3;
}
// Request message for
// [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].
message GetAnnotationSpecRequest {
// The resource name of the annotation spec to retrieve.
string name = 1;
}
// Request message for
// [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].
message CreateModelRequest {
@ -300,7 +365,7 @@ message ListModelsRequest {
// An expression for filtering the results of the request.
//
// * `model_metadata` - for existence of the case (e.g.
// video_classification_model_metadata:*).
// image_classification_model_metadata:*).
// * `dataset_id` - for = or !=. Some examples of using the filter are:
//
// * `image_classification_model_metadata:*` --> The model has
@ -349,6 +414,42 @@ message UpdateModelRequest {
google.protobuf.FieldMask update_mask = 2;
}
// Request message for
// [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].
message DeployModelRequest {
// The per-domain specific deployment parameters.
oneof model_deployment_metadata {
// Model deployment metadata specific to Image Object Detection.
ImageObjectDetectionModelDeploymentMetadata
image_object_detection_model_deployment_metadata = 2;
// Model deployment metadata specific to Image Classification.
ImageClassificationModelDeploymentMetadata
image_classification_model_deployment_metadata = 4;
}
// Resource name of the model to deploy.
string name = 1;
}
// Request message for
// [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].
message UndeployModelRequest {
// Resource name of the model to undeploy.
string name = 1;
}
// 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.
message ExportModelRequest {
// Required. The resource name of the model to export.
string name = 1;
// Required. The desired output location and configuration.
ModelExportOutputConfig output_config = 3;
}
// Request message for
// [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].
message GetModelEvaluationRequest {

60
google/cloud/automl/v1/text.proto Normal file
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@ -0,0 +1,60 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/cloud/automl/v1/classification.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "TextProto";
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Dataset metadata for classification.
message TextClassificationDatasetMetadata {
// Required. Type of the classification problem.
ClassificationType classification_type = 1;
}
// Model metadata that is specific to text classification.
message TextClassificationModelMetadata {
// Output only. Classification type of the dataset used to train this model.
ClassificationType classification_type = 3;
}
// Dataset metadata that is specific to text extraction
message TextExtractionDatasetMetadata {}
// Model metadata that is specific to text extraction.
message TextExtractionModelMetadata {}
// Dataset metadata for text sentiment.
message 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).
int32 sentiment_max = 1;
}
// Model metadata that is specific to text sentiment.
message TextSentimentModelMetadata {}

70
google/cloud/automl/v1/text_extraction.proto Normal file
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@ -0,0 +1,70 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/cloud/automl/v1/text_segment.proto";
import "google/api/annotations.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Annotation for identifying spans of text.
message TextExtractionAnnotation {
// Required. Text extraction annotations can either be a text segment or a
// text relation.
oneof annotation {
// An entity annotation will set this, which is the part of the original
// text to which the annotation pertains.
TextSegment text_segment = 3;
}
// Output only. A confidence estimate between 0.0 and 1.0. A higher value
// means greater confidence in correctness of the annotation.
float score = 1;
}
// Model evaluation metrics for text extraction problems.
message TextExtractionEvaluationMetrics {
// Metrics for a single confidence threshold.
message 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.
float confidence_threshold = 1;
// Output only. Recall under the given confidence threshold.
float recall = 3;
// Output only. Precision under the given confidence threshold.
float precision = 4;
// Output only. The harmonic mean of recall and precision.
float f1_score = 5;
}
// Output only. The Area under precision recall curve metric.
float au_prc = 1;
// Output only. Metrics that have confidence thresholds.
// Precision-recall curve can be derived from it.
repeated ConfidenceMetricsEntry confidence_metrics_entries = 2;
}

43
google/cloud/automl/v1/text_segment.proto Normal file
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@ -0,0 +1,43 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "TextSegmentProto";
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
message TextSegment {
// Output only. The content of the TextSegment.
string content = 3;
// Required. Zero-based character index of the first character of the text
// segment (counting characters from the beginning of the text).
int64 start_offset = 1;
// 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.
int64 end_offset = 2;
}

79
google/cloud/automl/v1/text_sentiment.proto Normal file
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@ -0,0 +1,79 @@
// Copyright 2019 Google LLC.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
syntax = "proto3";
package google.cloud.automl.v1;
import "google/api/annotations.proto";
import "google/cloud/automl/v1/classification.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "TextSentimentProto";
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoML\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// Contains annotation details specific to text sentiment.
message 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.
int32 sentiment = 1;
}
// Model evaluation metrics for text sentiment problems.
message TextSentimentEvaluationMetrics {
// Output only. Precision.
float precision = 1;
// Output only. Recall.
float recall = 2;
// Output only. The harmonic mean of recall and precision.
float f1_score = 3;
// Output only. Mean absolute error. Only set for the overall model
// evaluation, not for evaluation of a single annotation spec.
float mean_absolute_error = 4;
// Output only. Mean squared error. Only set for the overall model
// evaluation, not for evaluation of a single annotation spec.
float mean_squared_error = 5;
// Output only. Linear weighted kappa. Only set for the overall model
// evaluation, not for evaluation of a single annotation spec.
float linear_kappa = 6;
// Output only. Quadratic weighted kappa. Only set for the overall model
// evaluation, not for evaluation of a single annotation spec.
float quadratic_kappa = 7;
// Output only. Confusion matrix of the evaluation.
// Only set for the overall model evaluation, not for evaluation of a single
// annotation spec.
ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 8;
}

9
google/cloud/automl/v1/translation.proto
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@ -17,11 +17,12 @@ syntax = "proto3";
package google.cloud.automl.v1;
import "google/cloud/automl/v1/data_items.proto";
import "google/api/annotations.proto";
import "google/api/field_behavior.proto";
import "google/cloud/automl/v1/data_items.proto";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1;automl";
option java_multiple_files = true;
option java_outer_classname = "TranslationProto";
option java_package = "com.google.cloud.automl.v1";
@ -31,10 +32,10 @@ option ruby_package = "Google::Cloud::AutoML::V1";
// Dataset metadata that is specific to translation.
message TranslationDatasetMetadata {
// Required. The BCP-47 language code of the source language.
string source_language_code = 1;
string source_language_code = 1 [(google.api.field_behavior) = REQUIRED];
// Required. The BCP-47 language code of the target language.
string target_language_code = 2;
string target_language_code = 2 [(google.api.field_behavior) = REQUIRED];
}
// Evaluation metrics for the dataset.