mirror of
https://github.com/genuinetools/reg.git
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374 lines
12 KiB
Go
374 lines
12 KiB
Go
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// Code generated by protoc-gen-go. DO NOT EDIT.
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// source: google/cloud/ml/v1/prediction_service.proto
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package ml // import "google.golang.org/genproto/googleapis/cloud/ml/v1"
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import proto "github.com/golang/protobuf/proto"
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import fmt "fmt"
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import math "math"
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import _ "google.golang.org/genproto/googleapis/api/annotations"
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import httpbody "google.golang.org/genproto/googleapis/api/httpbody"
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import (
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context "golang.org/x/net/context"
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grpc "google.golang.org/grpc"
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)
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// Reference imports to suppress errors if they are not otherwise used.
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var _ = proto.Marshal
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var _ = fmt.Errorf
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var _ = math.Inf
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// This is a compile-time assertion to ensure that this generated file
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// is compatible with the proto package it is being compiled against.
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// A compilation error at this line likely means your copy of the
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// proto package needs to be updated.
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const _ = proto.ProtoPackageIsVersion2 // please upgrade the proto package
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// Request for predictions to be issued against a trained model.
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//
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// The body of the request is a single JSON object with a single top-level
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// field:
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//
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// <dl>
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// <dt>instances</dt>
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// <dd>A JSON array containing values representing the instances to use for
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// prediction.</dd>
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// </dl>
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//
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// The structure of each element of the instances list is determined by your
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// model's input definition. Instances can include named inputs or can contain
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// only unlabeled values.
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//
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// Not all data includes named inputs. Some instances will be simple
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// JSON values (boolean, number, or string). However, instances are often lists
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// of simple values, or complex nested lists. Here are some examples of request
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// bodies:
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//
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// CSV data with each row encoded as a string value:
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// <pre>
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// {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
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// </pre>
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// Plain text:
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// <pre>
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// {"instances": ["the quick brown fox", "la bruja le dio"]}
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// </pre>
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// Sentences encoded as lists of words (vectors of strings):
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// <pre>
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// {
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// "instances": [
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// ["the","quick","brown"],
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// ["la","bruja","le"],
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// ...
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// ]
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// }
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// </pre>
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// Floating point scalar values:
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// <pre>
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// {"instances": [0.0, 1.1, 2.2]}
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// </pre>
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// Vectors of integers:
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// <pre>
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// {
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// "instances": [
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// [0, 1, 2],
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// [3, 4, 5],
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// ...
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// ]
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// }
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// </pre>
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// Tensors (in this case, two-dimensional tensors):
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// <pre>
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// {
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// "instances": [
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// [
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// [0, 1, 2],
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// [3, 4, 5]
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// ],
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// ...
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// ]
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// }
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// </pre>
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// Images can be represented different ways. In this encoding scheme the first
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// two dimensions represent the rows and columns of the image, and the third
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// contains lists (vectors) of the R, G, and B values for each pixel.
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// <pre>
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// {
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// "instances": [
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// [
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// [
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// [138, 30, 66],
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// [130, 20, 56],
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// ...
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// ],
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// [
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// [126, 38, 61],
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// [122, 24, 57],
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// ...
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// ],
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// ...
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// ],
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// ...
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// ]
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// }
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// </pre>
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// JSON strings must be encoded as UTF-8. To send binary data, you must
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// base64-encode the data and mark it as binary. To mark a JSON string
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// as binary, replace it with a JSON object with a single attribute named `b64`:
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// <pre>{"b64": "..."} </pre>
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// For example:
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//
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// Two Serialized tf.Examples (fake data, for illustrative purposes only):
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// <pre>
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// {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
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// </pre>
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// Two JPEG image byte strings (fake data, for illustrative purposes only):
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// <pre>
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// {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
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// </pre>
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// If your data includes named references, format each instance as a JSON object
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// with the named references as the keys:
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//
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// JSON input data to be preprocessed:
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// <pre>
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// {
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// "instances": [
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// {
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// "a": 1.0,
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// "b": true,
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// "c": "x"
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// },
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// {
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// "a": -2.0,
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// "b": false,
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// "c": "y"
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// }
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// ]
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// }
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// </pre>
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// Some models have an underlying TensorFlow graph that accepts multiple input
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// tensors. In this case, you should use the names of JSON name/value pairs to
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// identify the input tensors, as shown in the following exmaples:
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//
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// For a graph with input tensor aliases "tag" (string) and "image"
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// (base64-encoded string):
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// <pre>
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// {
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// "instances": [
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// {
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// "tag": "beach",
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// "image": {"b64": "ASa8asdf"}
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// },
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// {
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// "tag": "car",
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// "image": {"b64": "JLK7ljk3"}
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// }
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// ]
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// }
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// </pre>
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// For a graph with input tensor aliases "tag" (string) and "image"
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// (3-dimensional array of 8-bit ints):
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// <pre>
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// {
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// "instances": [
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// {
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// "tag": "beach",
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// "image": [
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// [
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// [138, 30, 66],
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// [130, 20, 56],
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// ...
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// ],
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// [
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// [126, 38, 61],
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// [122, 24, 57],
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// ...
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// ],
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// ...
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// ]
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// },
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// {
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// "tag": "car",
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// "image": [
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// [
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// [255, 0, 102],
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// [255, 0, 97],
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// ...
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// ],
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// [
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// [254, 1, 101],
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// [254, 2, 93],
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// ...
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// ],
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// ...
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// ]
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// },
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// ...
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// ]
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// }
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// </pre>
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// If the call is successful, the response body will contain one prediction
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// entry per instance in the request body. If prediction fails for any
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// instance, the response body will contain no predictions and will contian
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// a single error entry instead.
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type PredictRequest struct {
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// Required. The resource name of a model or a version.
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//
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// Authorization: requires `Viewer` role on the parent project.
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Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
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//
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// Required. The prediction request body.
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HttpBody *httpbody.HttpBody `protobuf:"bytes,2,opt,name=http_body,json=httpBody,proto3" json:"http_body,omitempty"`
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XXX_NoUnkeyedLiteral struct{} `json:"-"`
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XXX_unrecognized []byte `json:"-"`
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XXX_sizecache int32 `json:"-"`
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}
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func (m *PredictRequest) Reset() { *m = PredictRequest{} }
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func (m *PredictRequest) String() string { return proto.CompactTextString(m) }
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func (*PredictRequest) ProtoMessage() {}
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func (*PredictRequest) Descriptor() ([]byte, []int) {
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return fileDescriptor_prediction_service_70f83d6188ceda1c, []int{0}
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}
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func (m *PredictRequest) XXX_Unmarshal(b []byte) error {
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return xxx_messageInfo_PredictRequest.Unmarshal(m, b)
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}
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func (m *PredictRequest) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
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return xxx_messageInfo_PredictRequest.Marshal(b, m, deterministic)
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}
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func (dst *PredictRequest) XXX_Merge(src proto.Message) {
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xxx_messageInfo_PredictRequest.Merge(dst, src)
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}
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func (m *PredictRequest) XXX_Size() int {
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return xxx_messageInfo_PredictRequest.Size(m)
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}
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func (m *PredictRequest) XXX_DiscardUnknown() {
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xxx_messageInfo_PredictRequest.DiscardUnknown(m)
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}
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var xxx_messageInfo_PredictRequest proto.InternalMessageInfo
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func (m *PredictRequest) GetName() string {
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if m != nil {
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return m.Name
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}
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return ""
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}
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func (m *PredictRequest) GetHttpBody() *httpbody.HttpBody {
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if m != nil {
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return m.HttpBody
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}
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return nil
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}
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func init() {
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proto.RegisterType((*PredictRequest)(nil), "google.cloud.ml.v1.PredictRequest")
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}
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// Reference imports to suppress errors if they are not otherwise used.
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var _ context.Context
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var _ grpc.ClientConn
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// This is a compile-time assertion to ensure that this generated file
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// is compatible with the grpc package it is being compiled against.
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const _ = grpc.SupportPackageIsVersion4
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// OnlinePredictionServiceClient is the client API for OnlinePredictionService service.
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//
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// For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.
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type OnlinePredictionServiceClient interface {
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// Performs prediction on the data in the request.
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//
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// **** REMOVE FROM GENERATED DOCUMENTATION
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Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.HttpBody, error)
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}
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type onlinePredictionServiceClient struct {
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cc *grpc.ClientConn
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}
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func NewOnlinePredictionServiceClient(cc *grpc.ClientConn) OnlinePredictionServiceClient {
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return &onlinePredictionServiceClient{cc}
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}
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func (c *onlinePredictionServiceClient) Predict(ctx context.Context, in *PredictRequest, opts ...grpc.CallOption) (*httpbody.HttpBody, error) {
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out := new(httpbody.HttpBody)
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err := c.cc.Invoke(ctx, "/google.cloud.ml.v1.OnlinePredictionService/Predict", in, out, opts...)
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if err != nil {
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return nil, err
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}
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return out, nil
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}
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// OnlinePredictionServiceServer is the server API for OnlinePredictionService service.
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type OnlinePredictionServiceServer interface {
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// Performs prediction on the data in the request.
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//
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// **** REMOVE FROM GENERATED DOCUMENTATION
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Predict(context.Context, *PredictRequest) (*httpbody.HttpBody, error)
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}
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func RegisterOnlinePredictionServiceServer(s *grpc.Server, srv OnlinePredictionServiceServer) {
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s.RegisterService(&_OnlinePredictionService_serviceDesc, srv)
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}
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func _OnlinePredictionService_Predict_Handler(srv interface{}, ctx context.Context, dec func(interface{}) error, interceptor grpc.UnaryServerInterceptor) (interface{}, error) {
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in := new(PredictRequest)
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if err := dec(in); err != nil {
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return nil, err
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}
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if interceptor == nil {
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return srv.(OnlinePredictionServiceServer).Predict(ctx, in)
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}
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info := &grpc.UnaryServerInfo{
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Server: srv,
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FullMethod: "/google.cloud.ml.v1.OnlinePredictionService/Predict",
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}
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handler := func(ctx context.Context, req interface{}) (interface{}, error) {
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return srv.(OnlinePredictionServiceServer).Predict(ctx, req.(*PredictRequest))
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}
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return interceptor(ctx, in, info, handler)
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}
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var _OnlinePredictionService_serviceDesc = grpc.ServiceDesc{
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ServiceName: "google.cloud.ml.v1.OnlinePredictionService",
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HandlerType: (*OnlinePredictionServiceServer)(nil),
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Methods: []grpc.MethodDesc{
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{
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MethodName: "Predict",
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Handler: _OnlinePredictionService_Predict_Handler,
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},
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},
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Streams: []grpc.StreamDesc{},
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Metadata: "google/cloud/ml/v1/prediction_service.proto",
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}
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func init() {
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proto.RegisterFile("google/cloud/ml/v1/prediction_service.proto", fileDescriptor_prediction_service_70f83d6188ceda1c)
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}
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var fileDescriptor_prediction_service_70f83d6188ceda1c = []byte{
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// 308 bytes of a gzipped FileDescriptorProto
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0xd5, 0x01, 0x00, 0x00,
|
||
|
}
|