| // 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 v3.12.2 |
| // 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 ImageObjectDetectionModelDeploymentMetadata.ProtoReflect.Descriptor instead. |
| func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor() ([]byte, []int) { |
| return file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP(), []int{5} |
| } |
| |
| func (x *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64 { |
| if x != nil { |
| return x.NodeCount |
| } |
| return 0 |
| } |
| |
| var File_google_cloud_automl_v1beta1_image_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_automl_v1beta1_image_proto_rawDesc = []byte{ |
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| 0x33, |
| } |
| |
| var ( |
| file_google_cloud_automl_v1beta1_image_proto_rawDescOnce sync.Once |
| file_google_cloud_automl_v1beta1_image_proto_rawDescData = file_google_cloud_automl_v1beta1_image_proto_rawDesc |
| ) |
| |
| func file_google_cloud_automl_v1beta1_image_proto_rawDescGZIP() []byte { |
| file_google_cloud_automl_v1beta1_image_proto_rawDescOnce.Do(func() { |
| file_google_cloud_automl_v1beta1_image_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_automl_v1beta1_image_proto_rawDescData) |
| }) |
| return 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 |
| } |
| } |
| file_google_cloud_automl_v1beta1_image_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ImageClassificationModelDeploymentMetadata); 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[5].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*ImageObjectDetectionModelDeploymentMetadata); i { |
| case 0: |
| return &v.state |
| case 1: |
| return &v.sizeCache |
| case 2: |
| return &v.unknownFields |
| default: |
| return nil |
| } |
| } |
| } |
| type x struct{} |
| out := protoimpl.TypeBuilder{ |
| File: protoimpl.DescBuilder{ |
| GoPackagePath: reflect.TypeOf(x{}).PkgPath(), |
| RawDescriptor: file_google_cloud_automl_v1beta1_image_proto_rawDesc, |
| NumEnums: 0, |
| NumMessages: 6, |
| NumExtensions: 0, |
| NumServices: 0, |
| }, |
| GoTypes: file_google_cloud_automl_v1beta1_image_proto_goTypes, |
| DependencyIndexes: file_google_cloud_automl_v1beta1_image_proto_depIdxs, |
| MessageInfos: 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 |
| } |
