| // Copyright 2017 Google Inc. |
| // |
| // 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/ml/v1/prediction_service.proto |
| |
| package ml |
| |
| import ( |
| context "context" |
| reflect "reflect" |
| sync "sync" |
| |
| proto "github.com/golang/protobuf/proto" |
| _ "google.golang.org/genproto/googleapis/api/annotations" |
| httpbody "google.golang.org/genproto/googleapis/api/httpbody" |
| grpc "google.golang.org/grpc" |
| codes "google.golang.org/grpc/codes" |
| status "google.golang.org/grpc/status" |
| 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 |
| |
| // Request for predictions to be issued against a trained model. |
| // |
| // The body of the request is a single JSON object with a single top-level |
| // field: |
| // |
| // <dl> |
| // <dt>instances</dt> |
| // <dd>A JSON array containing values representing the instances to use for |
| // prediction.</dd> |
| // </dl> |
| // |
| // The structure of each element of the instances list is determined by your |
| // model's input definition. Instances can include named inputs or can contain |
| // only unlabeled values. |
| // |
| // Not all data includes named inputs. Some instances will be simple |
| // JSON values (boolean, number, or string). However, instances are often lists |
| // of simple values, or complex nested lists. Here are some examples of request |
| // bodies: |
| // |
| // CSV data with each row encoded as a string value: |
| // <pre> |
| // {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]} |
| // </pre> |
| // Plain text: |
| // <pre> |
| // {"instances": ["the quick brown fox", "la bruja le dio"]} |
| // </pre> |
| // Sentences encoded as lists of words (vectors of strings): |
| // <pre> |
| // { |
| // "instances": [ |
| // ["the","quick","brown"], |
| // ["la","bruja","le"], |
| // ... |
| // ] |
| // } |
| // </pre> |
| // Floating point scalar values: |
| // <pre> |
| // {"instances": [0.0, 1.1, 2.2]} |
| // </pre> |
| // Vectors of integers: |
| // <pre> |
| // { |
| // "instances": [ |
| // [0, 1, 2], |
| // [3, 4, 5], |
| // ... |
| // ] |
| // } |
| // </pre> |
| // Tensors (in this case, two-dimensional tensors): |
| // <pre> |
| // { |
| // "instances": [ |
| // [ |
| // [0, 1, 2], |
| // [3, 4, 5] |
| // ], |
| // ... |
| // ] |
| // } |
| // </pre> |
| // Images can be represented different ways. In this encoding scheme the first |
| // two dimensions represent the rows and columns of the image, and the third |
| // contains lists (vectors) of the R, G, and B values for each pixel. |
| // <pre> |
| // { |
| // "instances": [ |
| // [ |
| // [ |
| // [138, 30, 66], |
| // [130, 20, 56], |
| // ... |
| // ], |
| // [ |
| // [126, 38, 61], |
| // [122, 24, 57], |
| // ... |
| // ], |
| // ... |
| // ], |
| // ... |
| // ] |
| // } |
| // </pre> |
| // JSON strings must be encoded as UTF-8. To send binary data, you must |
| // base64-encode the data and mark it as binary. To mark a JSON string |
| // as binary, replace it with a JSON object with a single attribute named `b64`: |
| // <pre>{"b64": "..."} </pre> |
| // For example: |
| // |
| // Two Serialized tf.Examples (fake data, for illustrative purposes only): |
| // <pre> |
| // {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]} |
| // </pre> |
| // Two JPEG image byte strings (fake data, for illustrative purposes only): |
| // <pre> |
| // {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]} |
| // </pre> |
| // If your data includes named references, format each instance as a JSON object |
| // with the named references as the keys: |
| // |
| // JSON input data to be preprocessed: |
| // <pre> |
| // { |
| // "instances": [ |
| // { |
| // "a": 1.0, |
| // "b": true, |
| // "c": "x" |
| // }, |
| // { |
| // "a": -2.0, |
| // "b": false, |
| // "c": "y" |
| // } |
| // ] |
| // } |
| // </pre> |
| // Some models have an underlying TensorFlow graph that accepts multiple input |
| // tensors. In this case, you should use the names of JSON name/value pairs to |
| // identify the input tensors, as shown in the following exmaples: |
| // |
| // For a graph with input tensor aliases "tag" (string) and "image" |
| // (base64-encoded string): |
| // <pre> |
| // { |
| // "instances": [ |
| // { |
| // "tag": "beach", |
| // "image": {"b64": "ASa8asdf"} |
| // }, |
| // { |
| // "tag": "car", |
| // "image": {"b64": "JLK7ljk3"} |
| // } |
| // ] |
| // } |
| // </pre> |
| // For a graph with input tensor aliases "tag" (string) and "image" |
| // (3-dimensional array of 8-bit ints): |
| // <pre> |
| // { |
| // "instances": [ |
| // { |
| // "tag": "beach", |
| // "image": [ |
| // [ |
| // [138, 30, 66], |
| // [130, 20, 56], |
| // ... |
| // ], |
| // [ |
| // [126, 38, 61], |
| // [122, 24, 57], |
| // ... |
| // ], |
| // ... |
| // ] |
| // }, |
| // { |
| // "tag": "car", |
| // "image": [ |
| // [ |
| // [255, 0, 102], |
| // [255, 0, 97], |
| // ... |
| // ], |
| // [ |
| // [254, 1, 101], |
| // [254, 2, 93], |
| // ... |
| // ], |
| // ... |
| // ] |
| // }, |
| // ... |
| // ] |
| // } |
| // </pre> |
| // If the call is successful, the response body will contain one prediction |
| // entry per instance in the request body. If prediction fails for any |
| // instance, the response body will contain no predictions and will contian |
| // a single error entry instead. |
| type PredictRequest struct { |
| state protoimpl.MessageState |
| sizeCache protoimpl.SizeCache |
| unknownFields protoimpl.UnknownFields |
| |
| // Required. The resource name of a model or a version. |
| // |
| // Authorization: requires `Viewer` role on the parent project. |
| Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"` |
| // |
| // Required. The prediction request body. |
| HttpBody *httpbody.HttpBody `protobuf:"bytes,2,opt,name=http_body,json=httpBody,proto3" json:"http_body,omitempty"` |
| } |
| |
| func (x *PredictRequest) Reset() { |
| *x = PredictRequest{} |
| if protoimpl.UnsafeEnabled { |
| mi := &file_google_cloud_ml_v1_prediction_service_proto_msgTypes[0] |
| ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x)) |
| ms.StoreMessageInfo(mi) |
| } |
| } |
| |
| func (x *PredictRequest) String() string { |
| return protoimpl.X.MessageStringOf(x) |
| } |
| |
| func (*PredictRequest) ProtoMessage() {} |
| |
| func (x *PredictRequest) ProtoReflect() protoreflect.Message { |
| mi := &file_google_cloud_ml_v1_prediction_service_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 PredictRequest.ProtoReflect.Descriptor instead. |
| func (*PredictRequest) Descriptor() ([]byte, []int) { |
| return file_google_cloud_ml_v1_prediction_service_proto_rawDescGZIP(), []int{0} |
| } |
| |
| func (x *PredictRequest) GetName() string { |
| if x != nil { |
| return x.Name |
| } |
| return "" |
| } |
| |
| func (x *PredictRequest) GetHttpBody() *httpbody.HttpBody { |
| if x != nil { |
| return x.HttpBody |
| } |
| return nil |
| } |
| |
| var File_google_cloud_ml_v1_prediction_service_proto protoreflect.FileDescriptor |
| |
| var file_google_cloud_ml_v1_prediction_service_proto_rawDesc = []byte{ |
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| 0x72, 0x6f, 0x74, 0x6f, 0x33, |
| } |
| |
| var ( |
| file_google_cloud_ml_v1_prediction_service_proto_rawDescOnce sync.Once |
| file_google_cloud_ml_v1_prediction_service_proto_rawDescData = file_google_cloud_ml_v1_prediction_service_proto_rawDesc |
| ) |
| |
| func file_google_cloud_ml_v1_prediction_service_proto_rawDescGZIP() []byte { |
| file_google_cloud_ml_v1_prediction_service_proto_rawDescOnce.Do(func() { |
| file_google_cloud_ml_v1_prediction_service_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_ml_v1_prediction_service_proto_rawDescData) |
| }) |
| return file_google_cloud_ml_v1_prediction_service_proto_rawDescData |
| } |
| |
| var file_google_cloud_ml_v1_prediction_service_proto_msgTypes = make([]protoimpl.MessageInfo, 1) |
| var file_google_cloud_ml_v1_prediction_service_proto_goTypes = []interface{}{ |
| (*PredictRequest)(nil), // 0: google.cloud.ml.v1.PredictRequest |
| (*httpbody.HttpBody)(nil), // 1: google.api.HttpBody |
| } |
| var file_google_cloud_ml_v1_prediction_service_proto_depIdxs = []int32{ |
| 1, // 0: google.cloud.ml.v1.PredictRequest.http_body:type_name -> google.api.HttpBody |
| 0, // 1: google.cloud.ml.v1.OnlinePredictionService.Predict:input_type -> google.cloud.ml.v1.PredictRequest |
| 1, // 2: google.cloud.ml.v1.OnlinePredictionService.Predict:output_type -> google.api.HttpBody |
| 2, // [2:3] is the sub-list for method output_type |
| 1, // [1:2] 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_ml_v1_prediction_service_proto_init() } |
| func file_google_cloud_ml_v1_prediction_service_proto_init() { |
| if File_google_cloud_ml_v1_prediction_service_proto != nil { |
| return |
| } |
| if !protoimpl.UnsafeEnabled { |
| file_google_cloud_ml_v1_prediction_service_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} { |
| switch v := v.(*PredictRequest); 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_ml_v1_prediction_service_proto_rawDesc, |
| NumEnums: 0, |
| NumMessages: 1, |
| NumExtensions: 0, |
| NumServices: 1, |
| }, |
| GoTypes: file_google_cloud_ml_v1_prediction_service_proto_goTypes, |
| DependencyIndexes: file_google_cloud_ml_v1_prediction_service_proto_depIdxs, |
| MessageInfos: file_google_cloud_ml_v1_prediction_service_proto_msgTypes, |
| }.Build() |
| File_google_cloud_ml_v1_prediction_service_proto = out.File |
| file_google_cloud_ml_v1_prediction_service_proto_rawDesc = nil |
| file_google_cloud_ml_v1_prediction_service_proto_goTypes = nil |
| file_google_cloud_ml_v1_prediction_service_proto_depIdxs = nil |
| } |
| |
| // Reference imports to suppress errors if they are not otherwise used. |
| var _ context.Context |
| var _ grpc.ClientConnInterface |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the grpc package it is being compiled against. |
| const _ = grpc.SupportPackageIsVersion6 |
| |
| // OnlinePredictionServiceClient is the client API for OnlinePredictionService service. |
| // |
| // For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream. |
| type OnlinePredictionServiceClient interface { |
| // Performs prediction on the data in the request. |
| // |
| // **** REMOVE FROM GENERATED DOCUMENTATION |
| Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.HttpBody, error) |
| } |
| |
| type onlinePredictionServiceClient struct { |
| cc grpc.ClientConnInterface |
| } |
| |
| func NewOnlinePredictionServiceClient(cc grpc.ClientConnInterface) OnlinePredictionServiceClient { |
| return &onlinePredictionServiceClient{cc} |
| } |
| |
| func (c *onlinePredictionServiceClient) Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.HttpBody, error) { |
| out := new(httpbody.HttpBody) |
| err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.OnlinePredictionService/Predict", in, out, opts...) |
| if err != nil { |
| return nil, err |
| } |
| return out, nil |
| } |
| |
| // OnlinePredictionServiceServer is the server API for OnlinePredictionService service. |
| type OnlinePredictionServiceServer interface { |
| // Performs prediction on the data in the request. |
| // |
| // **** REMOVE FROM GENERATED DOCUMENTATION |
| Predict(context.Context, *PredictRequest) (*httpbody.HttpBody, error) |
| } |
| |
| // UnimplementedOnlinePredictionServiceServer can be embedded to have forward compatible implementations. |
| type UnimplementedOnlinePredictionServiceServer struct { |
| } |
| |
| func (*UnimplementedOnlinePredictionServiceServer) Predict(context.Context, *PredictRequest) (*httpbody.HttpBody, error) { |
| return nil, status.Errorf(codes.Unimplemented, "method Predict not implemented") |
| } |
| |
| func RegisterOnlinePredictionServiceServer(s *grpc.Server, srv OnlinePredictionServiceServer) { |
| s.RegisterService(&_OnlinePredictionService_serviceDesc, srv) |
| } |
| |
| func _OnlinePredictionService_Predict_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) { |
| in := new(PredictRequest) |
| if err := dec(in); err != nil { |
| return nil, err |
| } |
| if interceptor == nil { |
| return srv.(OnlinePredictionServiceServer).Predict(ctx, in) |
| } |
| info := &grpc.UnaryServerInfo{ |
| Server: srv, |
| FullMethod: "/google.cloud.ml.v1.OnlinePredictionService/Predict", |
| } |
| handler := func(ctx context.Context, req interface{}) (interface{}, error) { |
| return srv.(OnlinePredictionServiceServer).Predict(ctx, req.(*PredictRequest)) |
| } |
| return interceptor(ctx, in, info, handler) |
| } |
| |
| var _OnlinePredictionService_serviceDesc = grpc.ServiceDesc{ |
| ServiceName: "google.cloud.ml.v1.OnlinePredictionService", |
| HandlerType: (*OnlinePredictionServiceServer)(nil), |
| Methods: []grpc.MethodDesc{ |
| { |
| MethodName: "Predict", |
| Handler: _OnlinePredictionService_Predict_Handler, |
| }, |
| }, |
| Streams: []grpc.StreamDesc{}, |
| Metadata: "google/cloud/ml/v1/prediction_service.proto", |
| } |