| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // source: google/cloud/automl/v1/image.proto |
| |
| package automl |
| |
| import ( |
| fmt "fmt" |
| math "math" |
| |
| proto "github.com/golang/protobuf/proto" |
| _ "github.com/golang/protobuf/ptypes/timestamp" |
| _ "google.golang.org/genproto/googleapis/api/annotations" |
| ) |
| |
| // Reference imports to suppress errors if they are not otherwise used. |
| var _ = proto.Marshal |
| var _ = fmt.Errorf |
| var _ = math.Inf |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the proto package it is being compiled against. |
| // A compilation error at this line likely means your copy of the |
| // proto package needs to be updated. |
| const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package |
| |
| // Dataset metadata that is specific to image classification. |
| type ImageClassificationDatasetMetadata struct { |
| // Required. Type of the classification problem. |
| ClassificationType ClassificationType `protobuf:"varint,1,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1.ClassificationType" json:"classification_type,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ImageClassificationDatasetMetadata) Reset() { *m = ImageClassificationDatasetMetadata{} } |
| func (m *ImageClassificationDatasetMetadata) String() string { return proto.CompactTextString(m) } |
| func (*ImageClassificationDatasetMetadata) ProtoMessage() {} |
| func (*ImageClassificationDatasetMetadata) Descriptor() ([]byte, []int) { |
| return fileDescriptor_651f5a2c51022614, []int{0} |
| } |
| |
| func (m *ImageClassificationDatasetMetadata) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ImageClassificationDatasetMetadata.Unmarshal(m, b) |
| } |
| func (m *ImageClassificationDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ImageClassificationDatasetMetadata.Marshal(b, m, deterministic) |
| } |
| func (m *ImageClassificationDatasetMetadata) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ImageClassificationDatasetMetadata.Merge(m, src) |
| } |
| func (m *ImageClassificationDatasetMetadata) XXX_Size() int { |
| return xxx_messageInfo_ImageClassificationDatasetMetadata.Size(m) |
| } |
| func (m *ImageClassificationDatasetMetadata) XXX_DiscardUnknown() { |
| xxx_messageInfo_ImageClassificationDatasetMetadata.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ImageClassificationDatasetMetadata proto.InternalMessageInfo |
| |
| func (m *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType { |
| if m != nil { |
| return m.ClassificationType |
| } |
| return ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED |
| } |
| |
| // Dataset metadata specific to image object detection. |
| type ImageObjectDetectionDatasetMetadata struct { |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ImageObjectDetectionDatasetMetadata) Reset() { *m = ImageObjectDetectionDatasetMetadata{} } |
| func (m *ImageObjectDetectionDatasetMetadata) String() string { return proto.CompactTextString(m) } |
| func (*ImageObjectDetectionDatasetMetadata) ProtoMessage() {} |
| func (*ImageObjectDetectionDatasetMetadata) Descriptor() ([]byte, []int) { |
| return fileDescriptor_651f5a2c51022614, []int{1} |
| } |
| |
| func (m *ImageObjectDetectionDatasetMetadata) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Unmarshal(m, b) |
| } |
| func (m *ImageObjectDetectionDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Marshal(b, m, deterministic) |
| } |
| func (m *ImageObjectDetectionDatasetMetadata) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Merge(m, src) |
| } |
| func (m *ImageObjectDetectionDatasetMetadata) XXX_Size() int { |
| return xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Size(m) |
| } |
| func (m *ImageObjectDetectionDatasetMetadata) XXX_DiscardUnknown() { |
| xxx_messageInfo_ImageObjectDetectionDatasetMetadata.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ImageObjectDetectionDatasetMetadata proto.InternalMessageInfo |
| |
| // Model metadata for image classification. |
| type ImageClassificationModelMetadata struct { |
| // 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"` |
| // 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. |
| TrainBudgetMilliNodeHours int64 `protobuf:"varint,16,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,17,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,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.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. |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ImageClassificationModelMetadata) Reset() { *m = ImageClassificationModelMetadata{} } |
| func (m *ImageClassificationModelMetadata) String() string { return proto.CompactTextString(m) } |
| func (*ImageClassificationModelMetadata) ProtoMessage() {} |
| func (*ImageClassificationModelMetadata) Descriptor() ([]byte, []int) { |
| return fileDescriptor_651f5a2c51022614, []int{2} |
| } |
| |
| func (m *ImageClassificationModelMetadata) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ImageClassificationModelMetadata.Unmarshal(m, b) |
| } |
| func (m *ImageClassificationModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ImageClassificationModelMetadata.Marshal(b, m, deterministic) |
| } |
| func (m *ImageClassificationModelMetadata) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ImageClassificationModelMetadata.Merge(m, src) |
| } |
| func (m *ImageClassificationModelMetadata) XXX_Size() int { |
| return xxx_messageInfo_ImageClassificationModelMetadata.Size(m) |
| } |
| func (m *ImageClassificationModelMetadata) XXX_DiscardUnknown() { |
| xxx_messageInfo_ImageClassificationModelMetadata.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ImageClassificationModelMetadata proto.InternalMessageInfo |
| |
| func (m *ImageClassificationModelMetadata) GetBaseModelId() string { |
| if m != nil { |
| return m.BaseModelId |
| } |
| return "" |
| } |
| |
| func (m *ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours() int64 { |
| if m != nil { |
| return m.TrainBudgetMilliNodeHours |
| } |
| return 0 |
| } |
| |
| func (m *ImageClassificationModelMetadata) GetTrainCostMilliNodeHours() int64 { |
| if m != nil { |
| return m.TrainCostMilliNodeHours |
| } |
| return 0 |
| } |
| |
| func (m *ImageClassificationModelMetadata) GetStopReason() string { |
| if m != nil { |
| return m.StopReason |
| } |
| return "" |
| } |
| |
| func (m *ImageClassificationModelMetadata) GetModelType() string { |
| if m != nil { |
| return m.ModelType |
| } |
| return "" |
| } |
| |
| func (m *ImageClassificationModelMetadata) GetNodeQps() float64 { |
| if m != nil { |
| return m.NodeQps |
| } |
| return 0 |
| } |
| |
| func (m *ImageClassificationModelMetadata) GetNodeCount() int64 { |
| if m != nil { |
| return m.NodeCount |
| } |
| return 0 |
| } |
| |
| // Model metadata specific to image object detection. |
| type ImageObjectDetectionModelMetadata struct { |
| // 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. |
| 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"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) Reset() { *m = ImageObjectDetectionModelMetadata{} } |
| func (m *ImageObjectDetectionModelMetadata) String() string { return proto.CompactTextString(m) } |
| func (*ImageObjectDetectionModelMetadata) ProtoMessage() {} |
| func (*ImageObjectDetectionModelMetadata) Descriptor() ([]byte, []int) { |
| return fileDescriptor_651f5a2c51022614, []int{3} |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ImageObjectDetectionModelMetadata.Unmarshal(m, b) |
| } |
| func (m *ImageObjectDetectionModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ImageObjectDetectionModelMetadata.Marshal(b, m, deterministic) |
| } |
| func (m *ImageObjectDetectionModelMetadata) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ImageObjectDetectionModelMetadata.Merge(m, src) |
| } |
| func (m *ImageObjectDetectionModelMetadata) XXX_Size() int { |
| return xxx_messageInfo_ImageObjectDetectionModelMetadata.Size(m) |
| } |
| func (m *ImageObjectDetectionModelMetadata) XXX_DiscardUnknown() { |
| xxx_messageInfo_ImageObjectDetectionModelMetadata.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ImageObjectDetectionModelMetadata proto.InternalMessageInfo |
| |
| func (m *ImageObjectDetectionModelMetadata) GetModelType() string { |
| if m != nil { |
| return m.ModelType |
| } |
| return "" |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) GetNodeCount() int64 { |
| if m != nil { |
| return m.NodeCount |
| } |
| return 0 |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) GetNodeQps() float64 { |
| if m != nil { |
| return m.NodeQps |
| } |
| return 0 |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) GetStopReason() string { |
| if m != nil { |
| return m.StopReason |
| } |
| return "" |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64 { |
| if m != nil { |
| return m.TrainBudgetMilliNodeHours |
| } |
| return 0 |
| } |
| |
| func (m *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64 { |
| if m != nil { |
| return m.TrainCostMilliNodeHours |
| } |
| return 0 |
| } |
| |
| // Model deployment metadata specific to Image Classification. |
| type ImageClassificationModelDeploymentMetadata struct { |
| // 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. |
| NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ImageClassificationModelDeploymentMetadata) Reset() { |
| *m = ImageClassificationModelDeploymentMetadata{} |
| } |
| func (m *ImageClassificationModelDeploymentMetadata) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*ImageClassificationModelDeploymentMetadata) ProtoMessage() {} |
| func (*ImageClassificationModelDeploymentMetadata) Descriptor() ([]byte, []int) { |
| return fileDescriptor_651f5a2c51022614, []int{4} |
| } |
| |
| func (m *ImageClassificationModelDeploymentMetadata) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Unmarshal(m, b) |
| } |
| func (m *ImageClassificationModelDeploymentMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Marshal(b, m, deterministic) |
| } |
| func (m *ImageClassificationModelDeploymentMetadata) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Merge(m, src) |
| } |
| func (m *ImageClassificationModelDeploymentMetadata) XXX_Size() int { |
| return xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Size(m) |
| } |
| func (m *ImageClassificationModelDeploymentMetadata) XXX_DiscardUnknown() { |
| xxx_messageInfo_ImageClassificationModelDeploymentMetadata.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ImageClassificationModelDeploymentMetadata proto.InternalMessageInfo |
| |
| func (m *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64 { |
| if m != nil { |
| return m.NodeCount |
| } |
| return 0 |
| } |
| |
| // Model deployment metadata specific to Image Object Detection. |
| type ImageObjectDetectionModelDeploymentMetadata struct { |
| // 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. |
| NodeCount int64 `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ImageObjectDetectionModelDeploymentMetadata) Reset() { |
| *m = ImageObjectDetectionModelDeploymentMetadata{} |
| } |
| func (m *ImageObjectDetectionModelDeploymentMetadata) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage() {} |
| func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor() ([]byte, []int) { |
| return fileDescriptor_651f5a2c51022614, []int{5} |
| } |
| |
| func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Unmarshal(m, b) |
| } |
| func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Marshal(b, m, deterministic) |
| } |
| func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Merge(m, src) |
| } |
| func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Size() int { |
| return xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Size(m) |
| } |
| func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_DiscardUnknown() { |
| xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata proto.InternalMessageInfo |
| |
| func (m *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64 { |
| if m != nil { |
| return m.NodeCount |
| } |
| return 0 |
| } |
| |
| func init() { |
| proto.RegisterType((*ImageClassificationDatasetMetadata)(nil), "google.cloud.automl.v1.ImageClassificationDatasetMetadata") |
| proto.RegisterType((*ImageObjectDetectionDatasetMetadata)(nil), "google.cloud.automl.v1.ImageObjectDetectionDatasetMetadata") |
| proto.RegisterType((*ImageClassificationModelMetadata)(nil), "google.cloud.automl.v1.ImageClassificationModelMetadata") |
| proto.RegisterType((*ImageObjectDetectionModelMetadata)(nil), "google.cloud.automl.v1.ImageObjectDetectionModelMetadata") |
| proto.RegisterType((*ImageClassificationModelDeploymentMetadata)(nil), "google.cloud.automl.v1.ImageClassificationModelDeploymentMetadata") |
| proto.RegisterType((*ImageObjectDetectionModelDeploymentMetadata)(nil), "google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata") |
| } |
| |
| func init() { |
| proto.RegisterFile("google/cloud/automl/v1/image.proto", fileDescriptor_651f5a2c51022614) |
| } |
| |
| var fileDescriptor_651f5a2c51022614 = []byte{ |
| // 572 bytes of a gzipped FileDescriptorProto |
| 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xa4, 0x54, 0xc1, 0x6e, 0xd3, 0x40, |
| 0x14, 0x94, 0x53, 0x68, 0xc8, 0x56, 0xad, 0xc0, 0x48, 0x25, 0x09, 0x54, 0x0d, 0x46, 0x48, 0x51, |
| 0x8b, 0x6c, 0x05, 0x6e, 0xa6, 0x07, 0x48, 0x22, 0x41, 0x45, 0x03, 0x25, 0x42, 0x39, 0x40, 0x24, |
| 0x6b, 0x63, 0x6f, 0xcd, 0x22, 0xdb, 0x6f, 0xf1, 0xae, 0x2b, 0xe5, 0xc8, 0x87, 0xf0, 0x03, 0x7c, |
| 0x00, 0x67, 0xce, 0x7c, 0x0a, 0x5f, 0x81, 0xf6, 0xad, 0x81, 0x38, 0x24, 0x54, 0xa8, 0xb7, 0xf8, |
| 0xcd, 0xcc, 0x9b, 0xf1, 0x6c, 0xd6, 0xc4, 0x89, 0x01, 0xe2, 0x84, 0x79, 0x61, 0x02, 0x45, 0xe4, |
| 0xd1, 0x42, 0x41, 0x9a, 0x78, 0xe7, 0x3d, 0x8f, 0xa7, 0x34, 0x66, 0xae, 0xc8, 0x41, 0x81, 0xbd, |
| 0x6b, 0x38, 0x2e, 0x72, 0x5c, 0xc3, 0x71, 0xcf, 0x7b, 0xed, 0x3b, 0xa5, 0x96, 0x0a, 0xee, 0xd1, |
| 0x2c, 0x03, 0x45, 0x15, 0x87, 0x4c, 0x1a, 0x55, 0xbb, 0xb5, 0x80, 0xe6, 0x4c, 0x42, 0x91, 0x87, |
| 0xe5, 0xc2, 0xf6, 0x83, 0x35, 0xa6, 0x7f, 0x96, 0x04, 0x52, 0xb0, 0xb0, 0x64, 0x1f, 0xae, 0x61, |
| 0x87, 0x09, 0x95, 0x92, 0x9f, 0xf1, 0x10, 0x15, 0x25, 0x79, 0xbf, 0x24, 0xe3, 0xd3, 0xac, 0x38, |
| 0xf3, 0x14, 0x4f, 0x99, 0x54, 0x34, 0x15, 0x86, 0xe0, 0x7c, 0xb2, 0x88, 0x73, 0xac, 0x5f, 0x6e, |
| 0x50, 0x91, 0x0f, 0xa9, 0xa2, 0x92, 0xa9, 0x11, 0x53, 0x34, 0xa2, 0x8a, 0xda, 0xef, 0xc8, 0xcd, |
| 0xea, 0xfe, 0x40, 0xcd, 0x05, 0x6b, 0x5a, 0x1d, 0xab, 0xbb, 0xf3, 0xf0, 0xc0, 0x5d, 0xdd, 0x88, |
| 0x5b, 0xdd, 0xf9, 0x66, 0x2e, 0xd8, 0xd8, 0x0e, 0xff, 0x9a, 0x39, 0xf7, 0xc9, 0x3d, 0x8c, 0xf0, |
| 0x6a, 0xf6, 0x81, 0x85, 0x6a, 0xc8, 0x14, 0x0b, 0x57, 0x64, 0x70, 0xbe, 0xd5, 0x48, 0x67, 0x45, |
| 0xd4, 0x11, 0x44, 0x2c, 0xf9, 0x1d, 0xd4, 0x21, 0xdb, 0x33, 0x2a, 0x59, 0x90, 0xea, 0x69, 0xc0, |
| 0x23, 0x8c, 0xd8, 0x18, 0x6f, 0xe9, 0x21, 0x32, 0x8f, 0x23, 0xfb, 0x09, 0xd9, 0x53, 0x39, 0xe5, |
| 0x59, 0x30, 0x2b, 0xa2, 0x98, 0xa9, 0x20, 0xe5, 0x49, 0xc2, 0x83, 0x0c, 0x22, 0x16, 0xbc, 0x87, |
| 0x22, 0x97, 0xcd, 0xeb, 0x1d, 0xab, 0xbb, 0x31, 0x6e, 0x21, 0xa9, 0x8f, 0x9c, 0x91, 0xa6, 0xbc, |
| 0x84, 0x88, 0x3d, 0xd7, 0x04, 0xfb, 0x88, 0xdc, 0x36, 0x1b, 0x42, 0x90, 0x2b, 0xf4, 0x37, 0x50, |
| 0x7f, 0x0b, 0x29, 0x03, 0x90, 0xcb, 0xea, 0x7d, 0xb2, 0x25, 0x15, 0x88, 0x20, 0x67, 0x54, 0x42, |
| 0xd6, 0xbc, 0x8a, 0x09, 0x89, 0x1e, 0x8d, 0x71, 0x62, 0xef, 0x11, 0x62, 0xf2, 0x63, 0xc9, 0x75, |
| 0xc4, 0x1b, 0x38, 0xd1, 0x7d, 0xd9, 0x2d, 0x72, 0x0d, 0xcd, 0x3e, 0x0a, 0xd9, 0xdc, 0xee, 0x58, |
| 0x5d, 0x6b, 0x5c, 0xd7, 0xcf, 0xaf, 0x85, 0xd4, 0x4a, 0x84, 0x42, 0x28, 0x32, 0xd5, 0xdc, 0xc1, |
| 0x1c, 0x0d, 0x3d, 0x19, 0xe8, 0x81, 0xf3, 0xb9, 0x46, 0xee, 0xae, 0xaa, 0xba, 0xda, 0x61, 0xd5, |
| 0xde, 0x5a, 0xb6, 0xaf, 0x7a, 0x6c, 0x2c, 0x79, 0x54, 0xd2, 0x5d, 0xa9, 0xa6, 0xbb, 0xf0, 0xc5, |
| 0x2f, 0x3c, 0x99, 0xcd, 0x4b, 0x9e, 0x4c, 0xfd, 0x9f, 0x27, 0xe3, 0xbc, 0x20, 0x07, 0xeb, 0xfe, |
| 0x61, 0x43, 0x26, 0x12, 0x98, 0xa7, 0x2c, 0x53, 0x8b, 0x3d, 0x2d, 0x14, 0x61, 0x2d, 0x97, 0x7d, |
| 0x42, 0x0e, 0xd7, 0x76, 0xfd, 0xdf, 0xdb, 0xfa, 0x5f, 0x2d, 0xd2, 0x0e, 0x21, 0x5d, 0x73, 0xd5, |
| 0xfa, 0x04, 0xad, 0x4e, 0xf5, 0x9d, 0x3e, 0xb5, 0xde, 0x1e, 0x95, 0xac, 0x18, 0x12, 0x9a, 0xc5, |
| 0x2e, 0xe4, 0xb1, 0x17, 0xb3, 0x0c, 0x6f, 0xbc, 0x67, 0x20, 0x2a, 0xb8, 0x5c, 0xfe, 0x84, 0x3c, |
| 0x36, 0xbf, 0xbe, 0xd4, 0x76, 0x9f, 0x19, 0xf9, 0x00, 0x4d, 0x9e, 0x16, 0x0a, 0x46, 0x27, 0xee, |
| 0xa4, 0xf7, 0xfd, 0x17, 0x30, 0x45, 0x60, 0x8a, 0x40, 0x32, 0x9d, 0xf4, 0x7e, 0xd4, 0x5a, 0x06, |
| 0xf0, 0x7d, 0x44, 0x7c, 0xdf, 0x68, 0x7c, 0x7f, 0xd2, 0x9b, 0x6d, 0xa2, 0xed, 0xa3, 0x9f, 0x01, |
| 0x00, 0x00, 0xff, 0xff, 0xea, 0xe9, 0x71, 0x14, 0x5b, 0x05, 0x00, 0x00, |
| } |
