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Diffstat (limited to 'third_party/googleapis/google/cloud/automl/v1beta1/image.proto')
| -rw-r--r-- | third_party/googleapis/google/cloud/automl/v1beta1/image.proto | 190 |
1 files changed, 190 insertions, 0 deletions
diff --git a/third_party/googleapis/google/cloud/automl/v1beta1/image.proto b/third_party/googleapis/google/cloud/automl/v1beta1/image.proto new file mode 100644 index 0000000..72f6871 --- /dev/null +++ b/third_party/googleapis/google/cloud/automl/v1beta1/image.proto @@ -0,0 +1,190 @@ +// Copyright 2020 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +syntax = "proto3"; + +package google.cloud.automl.v1beta1; + +import "google/cloud/automl/v1beta1/annotation_spec.proto"; +import "google/cloud/automl/v1beta1/classification.proto"; + +option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl"; +option java_multiple_files = true; +option java_outer_classname = "ImageProto"; +option java_package = "com.google.cloud.automl.v1beta1"; +option php_namespace = "Google\\Cloud\\AutoMl\\V1beta1"; +option ruby_package = "Google::Cloud::AutoML::V1beta1"; + +// 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; + + // Required. The train budget of creating this model, expressed in hours. The + // actual `train_cost` will be equal or less than this value. + int64 train_budget = 2; + + // Output only. The actual train cost of creating this model, expressed in + // hours. If this model is created from a `base` model, the train cost used + // to create the `base` model are not included. + int64 train_cost = 3; + + // 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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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.v1beta1.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. + // * `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.v1beta1.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.v1beta1.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.v1beta1.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. + 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.v1beta1.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.v1beta1.ImageObjectDetectionModelMetadata.qps_per_node]. + // Must be between 1 and 100, inclusive on both ends. + int64 node_count = 1; +} |