// Copyright 2020 Google LLC // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. // Code generated by protoc-gen-go. DO NOT EDIT. // versions: // protoc-gen-go v1.22.0 // protoc v3.11.2 // source: google/cloud/automl/v1beta1/image.proto package automl import ( reflect "reflect" sync "sync" proto "github.com/golang/protobuf/proto" _ "github.com/golang/protobuf/ptypes/timestamp" _ "google.golang.org/genproto/googleapis/api/annotations" protoreflect "google.golang.org/protobuf/reflect/protoreflect" protoimpl "google.golang.org/protobuf/runtime/protoimpl" ) const ( // Verify that this generated code is sufficiently up-to-date. _ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion) // Verify that runtime/protoimpl is sufficiently up-to-date. _ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20) ) // This is a compile-time assertion that a sufficiently up-to-date version // of the legacy proto package is being used. const _ = proto.ProtoPackageIsVersion4 // Dataset metadata that is specific to image classification. type ImageClassificationDatasetMetadata struct { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields // Required. Type of the classification problem. ClassificationType ClassificationType `protobuf:"varint,1,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1beta1.ClassificationType" json:"classification_type,omitempty"` } func (x *ImageClassificationDatasetMetadata) Reset() { *x = ImageClassificationDatasetMetadata{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[0] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *ImageClassificationDatasetMetadata) String() string { return protoimpl.X.MessageStringOf(x) } func (*ImageClassificationDatasetMetadata) ProtoMessage() {} func (x *ImageClassificationDatasetMetadata) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[0] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use ImageClassificationDatasetMetadata.ProtoReflect.Descriptor instead. func (*ImageClassificationDatasetMetadata) Descriptor() ([]byte, []int) { return file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP(), []int{0} } func (x *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType { if x != nil { return x.ClassificationType } return ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED } // Dataset metadata specific to image object detection. type ImageObjectDetectionDatasetMetadata struct { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields } func (x *ImageObjectDetectionDatasetMetadata) Reset() { *x = ImageObjectDetectionDatasetMetadata{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[1] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *ImageObjectDetectionDatasetMetadata) String() string { return protoimpl.X.MessageStringOf(x) } func (*ImageObjectDetectionDatasetMetadata) ProtoMessage() {} func (x *ImageObjectDetectionDatasetMetadata) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[1] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use ImageObjectDetectionDatasetMetadata.ProtoReflect.Descriptor instead. func (*ImageObjectDetectionDatasetMetadata) Descriptor() ([]byte, []int) { return file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP(), []int{1} } // Model metadata for image classification. type ImageClassificationModelMetadata struct { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields // 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`. BaseModelId string `protobuf:"bytes,1,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"` // Required. The train budget of creating this model, expressed in hours. The // actual `train_cost` will be equal or less than this value. TrainBudget int64 `protobuf:"varint,2,opt,name=train_budget,json=trainBudget,proto3" json:"train_budget,omitempty"` // 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. TrainCost int64 `protobuf:"varint,3,opt,name=train_cost,json=trainCost,proto3" json:"train_cost,omitempty"` // Output only. The reason that this create model operation stopped, // e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`. StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"` // 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. ModelType string `protobuf:"bytes,7,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"` // Output only. An approximate number of online prediction QPS that can // be supported by this model per each node on which it is deployed. NodeQps float64 `protobuf:"fixed64,13,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"` // 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. NodeCount int64 `protobuf:"varint,14,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"` } func (x *ImageClassificationModelMetadata) Reset() { *x = ImageClassificationModelMetadata{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[2] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *ImageClassificationModelMetadata) String() string { return protoimpl.X.MessageStringOf(x) } func (*ImageClassificationModelMetadata) ProtoMessage() {} func (x *ImageClassificationModelMetadata) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[2] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use ImageClassificationModelMetadata.ProtoReflect.Descriptor instead. func (*ImageClassificationModelMetadata) Descriptor() ([]byte, []int) { return file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP(), []int{2} } func (x *ImageClassificationModelMetadata) GetBaseModelId() string { if x != nil { return x.BaseModelId } return "" } func (x *ImageClassificationModelMetadata) GetTrainBudget() int64 { if x != nil { return x.TrainBudget } return 0 } func (x *ImageClassificationModelMetadata) GetTrainCost() int64 { if x != nil { return x.TrainCost } return 0 } func (x *ImageClassificationModelMetadata) GetStopReason() string { if x != nil { return x.StopReason } return "" } func (x *ImageClassificationModelMetadata) GetModelType() string { if x != nil { return x.ModelType } return "" } func (x *ImageClassificationModelMetadata) GetNodeQps() float64 { if x != nil { return x.NodeQps } return 0 } func (x *ImageClassificationModelMetadata) GetNodeCount() int64 { if x != nil { return x.NodeCount } return 0 } // Model metadata specific to image object detection. type ImageObjectDetectionModelMetadata struct { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields // 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. ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"` // 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. NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"` // Output only. An approximate number of online prediction QPS that can // be supported by this model per each node on which it is deployed. NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"` // Output only. The reason that this create model operation stopped, // e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`. StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"` // 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. TrainBudgetMilliNodeHours int64 `protobuf:"varint,6,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"` // 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. TrainCostMilliNodeHours int64 `protobuf:"varint,7,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"` } func (x *ImageObjectDetectionModelMetadata) Reset() { *x = ImageObjectDetectionModelMetadata{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[3] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *ImageObjectDetectionModelMetadata) String() string { return protoimpl.X.MessageStringOf(x) } func (*ImageObjectDetectionModelMetadata) ProtoMessage() {} func (x *ImageObjectDetectionModelMetadata) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[3] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use ImageObjectDetectionModelMetadata.ProtoReflect.Descriptor instead. func (*ImageObjectDetectionModelMetadata) Descriptor() ([]byte, []int) { return file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP(), []int{3} } func (x *ImageObjectDetectionModelMetadata) GetModelType() string { if x != nil { return x.ModelType } return "" } func (x *ImageObjectDetectionModelMetadata) GetNodeCount() int64 { if x != nil { return x.NodeCount } return 0 } func (x *ImageObjectDetectionModelMetadata) GetNodeQps() float64 { if x != nil { return x.NodeQps } return 0 } func (x *ImageObjectDetectionModelMetadata) GetStopReason() string { if x != nil { return x.StopReason } return "" } func (x *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64 { if x != nil { return x.TrainBudgetMilliNodeHours } return 0 } func (x *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64 { if x != nil { return x.TrainCostMilliNodeHours } return 0 } // Model deployment metadata specific to Image Classification. type ImageClassificationModelDeploymentMetadata struct { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields // 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. NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"` } func (x *ImageClassificationModelDeploymentMetadata) Reset() { *x = ImageClassificationModelDeploymentMetadata{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[4] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *ImageClassificationModelDeploymentMetadata) String() string { return protoimpl.X.MessageStringOf(x) } func (*ImageClassificationModelDeploymentMetadata) ProtoMessage() {} func (x *ImageClassificationModelDeploymentMetadata) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[4] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use ImageClassificationModelDeploymentMetadata.ProtoReflect.Descriptor instead. func (*ImageClassificationModelDeploymentMetadata) Descriptor() ([]byte, []int) { return file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP(), []int{4} } func (x *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64 { if x != nil { return x.NodeCount } return 0 } // Model deployment metadata specific to Image Object Detection. type ImageObjectDetectionModelDeploymentMetadata struct { state protoimpl.MessageState sizeCache protoimpl.SizeCache unknownFields protoimpl.UnknownFields // 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. NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"` } func (x *ImageObjectDetectionModelDeploymentMetadata) Reset() { *x = ImageObjectDetectionModelDeploymentMetadata{} if protoimpl.UnsafeEnabled { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[5] ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) ms.StoreMessageInfo(mi) } } func (x *ImageObjectDetectionModelDeploymentMetadata) String() string { return protoimpl.X.MessageStringOf(x) } func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage() {} func (x *ImageObjectDetectionModelDeploymentMetadata) ProtoReflect() protoreflect.Message { mi := &file_google_cloud_automl_v1beta1_image_proto_msgTypes[5] if protoimpl.UnsafeEnabled && x != nil { ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) if ms.LoadMessageInfo() == nil { ms.StoreMessageInfo(mi) } return ms } return mi.MessageOf(x) } // Deprecated: Use 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file_google_cloud_automl_v1beta1_image_proto_rawDescData } var file_google_cloud_automl_v1beta1_image_proto_msgTypes = make([]protoimpl.MessageInfo, 6) var file_google_cloud_automl_v1beta1_image_proto_goTypes = []interface{}{ (*ImageClassificationDatasetMetadata)(nil), // 0: google.cloud.automl.v1beta1.ImageClassificationDatasetMetadata (*ImageObjectDetectionDatasetMetadata)(nil), // 1: google.cloud.automl.v1beta1.ImageObjectDetectionDatasetMetadata (*ImageClassificationModelMetadata)(nil), // 2: google.cloud.automl.v1beta1.ImageClassificationModelMetadata (*ImageObjectDetectionModelMetadata)(nil), // 3: google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata (*ImageClassificationModelDeploymentMetadata)(nil), // 4: google.cloud.automl.v1beta1.ImageClassificationModelDeploymentMetadata (*ImageObjectDetectionModelDeploymentMetadata)(nil), // 5: google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata (ClassificationType)(0), // 6: google.cloud.automl.v1beta1.ClassificationType } var file_google_cloud_automl_v1beta1_image_proto_depIdxs = []int32{ 6, // 0: google.cloud.automl.v1beta1.ImageClassificationDatasetMetadata.classification_type:type_name -> google.cloud.automl.v1beta1.ClassificationType 1, // [1:1] is the sub-list for method output_type 1, // [1:1] is the sub-list for method input_type 1, // [1:1] is the sub-list for extension type_name 1, // [1:1] is the sub-list for extension extendee 0, // [0:1] is the sub-list for field type_name } func init() { file_google_cloud_automl_v1beta1_image_proto_init() } func file_google_cloud_automl_v1beta1_image_proto_init() { if File_google_cloud_automl_v1beta1_image_proto != nil { return } file_google_cloud_automl_v1beta1_annotation_spec_proto_init() file_google_cloud_automl_v1beta1_classification_proto_init() if !protoimpl.UnsafeEnabled { file_google_cloud_automl_v1beta1_image_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { switch v := v.(*ImageClassificationDatasetMetadata); i { case 0: return &v.state case 1: return &v.sizeCache case 2: return &v.unknownFields default: return nil } } file_google_cloud_automl_v1beta1_image_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} { switch v := v.(*ImageObjectDetectionDatasetMetadata); i { case 0: return &v.state case 1: return &v.sizeCache case 2: return &v.unknownFields default: return nil } } file_google_cloud_automl_v1beta1_image_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} { switch v := v.(*ImageClassificationModelMetadata); i { case 0: return &v.state case 1: return &v.sizeCache case 2: return &v.unknownFields default: return nil } } file_google_cloud_automl_v1beta1_image_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} { switch v := v.(*ImageObjectDetectionModelMetadata); i { case 0: return &v.state case 1: return &v.sizeCache case 2: return &v.unknownFields default: return nil } } 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file_google_cloud_automl_v1beta1_image_proto_msgTypes, }.Build() File_google_cloud_automl_v1beta1_image_proto = out.File file_google_cloud_automl_v1beta1_image_proto_rawDesc = nil file_google_cloud_automl_v1beta1_image_proto_goTypes = nil file_google_cloud_automl_v1beta1_image_proto_depIdxs = nil }