Synchronize new proto/yaml changes.
PiperOrigin-RevId: 275873210
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Google APIs
Copybara-Service
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@ -17,8 +17,8 @@ syntax = "proto3";
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package google.cloud.automl.v1beta1;
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import "google/cloud/automl/v1beta1/temporal.proto";
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import "google/api/annotations.proto";
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import "google/cloud/automl/v1beta1/temporal.proto";
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option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
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option java_outer_classname = "ClassificationProto";
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@ -126,7 +126,10 @@ message ClassificationEvaluationMetrics {
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// for each example.
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float false_positive_rate_at1 = 9;
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// Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
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// Output only. The harmonic mean of
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// [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1]
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// and
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// [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
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float f1_score_at1 = 7;
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// Output only. The number of model created labels that match a ground truth
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@ -153,7 +156,9 @@ message ClassificationEvaluationMetrics {
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// Output only. Value of the specific cell in the confusion matrix.
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// The number of values each row has (i.e. the length of the row) is equal
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// to the length of the `annotation_spec_id` field or, if that one is not
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// populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
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// populated, length of the
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// [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name]
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// field.
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repeated int32 example_count = 1;
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}
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@ -175,9 +180,9 @@ message ClassificationEvaluationMetrics {
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// Output only. Rows in the confusion matrix. The number of rows is equal to
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// the size of `annotation_spec_id`.
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// `row[i].value[j]` is the number of examples that have ground truth of the
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// `annotation_spec_id[i]` and are predicted as `annotation_spec_id[j]` by
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// the model being evaluated.
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// `row[i].example_count[j]` is the number of examples that have ground
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// truth of the `annotation_spec_id[i]` and are predicted as
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// `annotation_spec_id[j]` by the model being evaluated.
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repeated Row row = 2;
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}
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@ -35,9 +35,11 @@ option ruby_package = "Google::Cloud::AutoML::V1beta1";
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// Only images up to 30MB in size are supported.
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message Image {
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// Input only. The data representing the image.
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// For Predict calls [image_bytes][] must be set, as other options are not
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// currently supported by prediction API. You can read the contents of an
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// uploaded image by using the [content_uri][] field.
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// For Predict calls
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// [image_bytes][google.cloud.automl.v1beta1.Image.image_bytes] must be set,
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// as other options are not currently supported by prediction API. You can
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// read the contents of an uploaded image by using the
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// [content_uri][google.cloud.automl.v1beta1.Image.content_uri] field.
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oneof data {
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// Image content represented as a stream of bytes.
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// Note: As with all `bytes` fields, protobuffers use a pure binary
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@ -98,6 +98,15 @@ message ImageClassificationModelMetadata {
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// to have a higher latency, but should also have a higher
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// prediction quality than other models.
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string model_type = 7;
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// Output only. An approximate number of online prediction QPS that can
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// be supported by this model per each node on which it is deployed.
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double node_qps = 13;
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// Output only. The number of nodes this model is deployed on. A node is an
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// abstraction of a machine resource, which can handle online prediction QPS
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// as given in the node_qps field.
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int64 node_count = 14;
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}
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// Model metadata specific to image object detection.
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@ -132,7 +141,7 @@ message ImageObjectDetectionModelMetadata {
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// full budget and the stop_reason will be `MODEL_CONVERGED`.
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// Note, node_hour = actual_hour * number_of_nodes_invovled.
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// For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
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// the train budget must be between 20,000 and 2,000,000 milli node hours,
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// the train budget must be between 20,000 and 900,000 milli node hours,
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// inclusive. The default value is 216, 000 which represents one day in
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// wall time.
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// For model type `mobile-low-latency-1`, `mobile-versatile-1`,
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@ -153,7 +162,8 @@ message ImageClassificationModelDeploymentMetadata {
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// Input only. The number of nodes to deploy the model on. A node is an
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// abstraction of a machine resource, which can handle online prediction QPS
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// as given in the model's
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// [node_qps][google.cloud.automl.v1p1beta.ImageClassificationModelMetadata.node_qps].
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//
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// [node_qps][google.cloud.automl.v1beta1.ImageClassificationModelMetadata.node_qps].
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// Must be between 1 and 100, inclusive on both ends.
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int64 node_count = 1;
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}
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@ -986,7 +986,7 @@ message ModelExportOutputConfig {
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oneof destination {
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// The Google Cloud Storage location where the model is to be written to.
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// This location may only be set for the following model formats:
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// "tflite", "edgetpu_tflite", "core_ml", "docker".
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// "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml".
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//
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// Under the directory given as the destination a new one with name
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// "model-export-<model-display-name>-<timestamp-of-export-call>",
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@ -1010,7 +1010,8 @@ message ModelExportOutputConfig {
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//
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// * For Image Classification mobile-low-latency-1, mobile-versatile-1,
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// mobile-high-accuracy-1:
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// "tflite" (default), "edgetpu_tflite", "tf_saved_model", "docker".
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// "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js",
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// "docker".
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//
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// * For Image Classification mobile-core-ml-low-latency-1,
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// mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
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@ -1021,6 +1022,8 @@ message ModelExportOutputConfig {
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// * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/)
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// devices.
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// * tf_saved_model - A tensorflow model in SavedModel format.
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// * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can
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// be used in the browser and in Node.js using JavaScript.
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// * docker - Used for Docker containers. Use the params field to customize
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// the container. The container is verified to work correctly on
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// ubuntu 16.04 operating system. See more at
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@ -17,13 +17,13 @@ syntax = "proto3";
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package google.cloud.automl.v1beta1;
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import "google/api/annotations.proto";
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import "google/cloud/automl/v1beta1/io.proto";
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import "google/cloud/automl/v1beta1/model.proto";
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import "google/cloud/automl/v1beta1/model_evaluation.proto";
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import "google/protobuf/empty.proto";
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import "google/protobuf/timestamp.proto";
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import "google/rpc/status.proto";
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import "google/api/annotations.proto";
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option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
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option java_multiple_files = true;
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@ -61,7 +61,8 @@ message OperationMetadata {
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ExportModelOperationMetadata export_model_details = 22;
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// Details of ExportEvaluatedExamples operation.
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ExportEvaluatedExamplesOperationMetadata export_evaluated_examples_details = 26;
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ExportEvaluatedExamplesOperationMetadata export_evaluated_examples_details =
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26;
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}
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// Output only. Progress of operation. Range: [0, 100].
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@ -82,29 +83,19 @@ message OperationMetadata {
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}
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// Details of operations that perform deletes of any entities.
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message DeleteOperationMetadata {
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}
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message DeleteOperationMetadata {}
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// Details of DeployModel operation.
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message DeployModelOperationMetadata {
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}
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message DeployModelOperationMetadata {}
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// Details of UndeployModel operation.
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message UndeployModelOperationMetadata {
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}
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message UndeployModelOperationMetadata {}
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// Details of CreateModel operation.
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message CreateModelOperationMetadata {
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}
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message CreateModelOperationMetadata {}
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// Details of ImportData operation.
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message ImportDataOperationMetadata {
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}
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message ImportDataOperationMetadata {}
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// Details of ExportData operation.
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message ExportDataOperationMetadata {
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@ -17,6 +17,7 @@ syntax = "proto3";
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package google.cloud.automl.v1beta1;
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import "google/api/annotations.proto";
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import "google/cloud/automl/v1beta1/classification.proto";
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import "google/cloud/automl/v1beta1/column_spec.proto";
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import "google/cloud/automl/v1beta1/data_items.proto";
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@ -25,7 +26,6 @@ import "google/cloud/automl/v1beta1/ranges.proto";
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import "google/cloud/automl/v1beta1/temporal.proto";
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import "google/protobuf/struct.proto";
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import "google/protobuf/timestamp.proto";
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import "google/api/annotations.proto";
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option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
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option java_multiple_files = true;
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@ -103,6 +103,19 @@ message TablesDatasetMetadata {
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// Model metadata specific to AutoML Tables.
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message TablesModelMetadata {
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// Additional optimization objective configuration. Required for
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// `MAXIMIZE_PRECISION_AT_RECALL` and `MAXIMIZE_RECALL_AT_PRECISION`,
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// otherwise unused.
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oneof additional_optimization_objective_config {
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// Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
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// Must be between 0 and 1, inclusive.
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float optimization_objective_recall_value = 17;
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// Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
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// Must be between 0 and 1, inclusive.
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float optimization_objective_precision_value = 18;
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}
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// Column spec of the dataset's primary table's column the model is
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// predicting. Snapshotted when model creation started.
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// Only 3 fields are used:
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@ -17,8 +17,8 @@ syntax = "proto3";
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package google.cloud.automl.v1beta1;
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import "google/cloud/automl/v1beta1/classification.proto";
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import "google/api/annotations.proto";
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import "google/cloud/automl/v1beta1/classification.proto";
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option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
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option java_multiple_files = true;
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@ -35,31 +35,25 @@ message TextClassificationDatasetMetadata {
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// Model metadata that is specific to text classification.
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message TextClassificationModelMetadata {
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// Output only. Classification type of the dataset used to train this model.
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ClassificationType classification_type = 3;
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}
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// Dataset metadata that is specific to text extraction
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message TextExtractionDatasetMetadata {
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}
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message TextExtractionDatasetMetadata {}
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// Model metadata that is specific to text extraction.
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message TextExtractionModelMetadata {
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}
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message TextExtractionModelMetadata {}
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// Dataset metadata for text sentiment.
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message TextSentimentDatasetMetadata {
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// Required. A sentiment is expressed as an integer ordinal, where higher value
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// means a more positive sentiment. The range of sentiments that will be used
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// is between 0 and sentiment_max (inclusive on both ends), and all the values
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// in the range must be represented in the dataset before a model can be
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// created.
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// sentiment_max value must be between 1 and 10 (inclusive).
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// Required. A sentiment is expressed as an integer ordinal, where higher
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// value means a more positive sentiment. The range of sentiments that will be
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// used is between 0 and sentiment_max (inclusive on both ends), and all the
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// values in the range must be represented in the dataset before a model can
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// be created. sentiment_max value must be between 1 and 10 (inclusive).
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int32 sentiment_max = 1;
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}
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// Model metadata that is specific to text sentiment.
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message TextSentimentModelMetadata {
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}
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message TextSentimentModelMetadata {}
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