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predictor.go
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predictor.go
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package savedmodel
import (
"context"
"fmt"
"os"
"path"
"strconv"
"sync"
"unsafe"
"github.com/Applifier/go-tensorflow/internal/typeconv"
"github.com/Applifier/go-tensorflow/predict"
"github.com/Applifier/go-tensorflow/serving"
"github.com/Applifier/go-tensorflow/utils"
tf "github.com/tensorflow/tensorflow/tensorflow/go"
)
const defaultBufferSize = 2048
type savedModelPredictor struct {
runner *Runner
name string
version int
model *tf.SavedModel
bufferPool sync.Pool
}
// NewPredictor returns a new predictor (predict.Predictor) for a given saved model folder path name and version
func NewPredictor(modelsDir string, name string, version int, signature string) (predict.Predictor, error) {
tags := []string{"serve"}
modelPath := path.Join(modelsDir, name, strconv.Itoa(version))
file, err := os.Open(path.Join(modelPath, "saved_model.pb"))
if err != nil {
return nil, err
}
defer file.Close()
signatureDef, err := GetSignatureDefFromReader(tags, signature, file)
if err != nil {
return nil, err
}
model, err := tf.LoadSavedModel(modelPath, tags, nil)
if err != nil {
return nil, err
}
runner, err := NewRunnerWithSignature(model, signatureDef)
if err != nil {
return nil, err
}
return &savedModelPredictor{
runner: runner,
name: name,
version: version,
model: model,
bufferPool: sync.Pool{
New: func() interface{} {
return make([]byte, 0, defaultBufferSize)
},
},
}, nil
}
func (ep *savedModelPredictor) getBuffer(size int) []byte {
buf := ep.bufferPool.Get().([]byte)
if cap(buf) >= size {
return buf[:size]
}
return make([]byte, size)
}
func (ep *savedModelPredictor) putBuffer(b []byte) {
ep.bufferPool.Put(b)
}
func (ep *savedModelPredictor) convertValueToTensor(val interface{}) (*tf.Tensor, error) {
switch v := val.(type) {
case *tf.Tensor:
return v, nil
case *serving.Tensor:
return tf.NewTensor(serving.ValueFromTensor(v))
case *predict.Example:
exampleSerialized, err := v.Marshal()
if err != nil {
return nil, err
}
return tf.NewTensor([]string{string(exampleSerialized)})
case predict.Examplifier:
examples, err := v.Examples()
if err != nil {
return nil, err
}
examplesStrings := make([]string, len(examples))
for i, example := range examples {
exampleSerialized, err := example.Marshal()
if err != nil {
return nil, err
}
examplesStrings[i] = string(exampleSerialized)
}
return tf.NewTensor(examplesStrings)
case map[string]interface{}:
example, err := utils.NewExampleFromMap(v)
if err != nil {
return nil, err
}
exampleSerialized, err := example.Marshal()
if err != nil {
return nil, err
}
return tf.NewTensor([]string{string(exampleSerialized)})
case []map[string]interface{}:
examples := make([]string, len(v))
for i, m := range v {
example, err := utils.NewExampleFromMap(m)
if err != nil {
return nil, err
}
exampleSerialized, err := example.Marshal()
if err != nil {
return nil, err
}
examples[i] = string(exampleSerialized)
}
return tf.NewTensor(examples)
case []interface{}:
typedSlice, err := typeconv.ConvertInterfaceSliceToTypedSlice(v)
if err != nil {
return nil, err
}
return ep.convertValueToTensor(typedSlice)
}
return tf.NewTensor(val)
}
func (ep *savedModelPredictor) Predict(ctx context.Context, inputs map[string]interface{}, outputFilter []string) (map[string]predict.Tensor, predict.ModelInfo, error) {
inputTensorMap := make(map[string]*tf.Tensor, len(inputs))
for key, val := range inputs {
var err error
inputTensorMap[key], err = ep.convertValueToTensor(val)
if err != nil {
return nil, predict.ModelInfo{}, err
}
}
res, err := ep.runner.Run(inputTensorMap, outputFilter)
if err != nil {
return nil, predict.ModelInfo{}, err
}
outputMap := make(map[string]predict.Tensor, len(res))
for key, tensor := range res {
outputMap[key] = &savedModelPredictorTensor{t: tensor}
}
return outputMap, predict.ModelInfo{
Name: ep.name,
Version: ep.version,
}, nil
}
func (ep *savedModelPredictor) marshalExample(e *predict.Example) ([]byte, error) {
buf := ep.getBuffer(e.Size())
n, err := e.MarshalTo(buf)
if err != nil {
return nil, err
}
return buf[:n], nil
}
func (ep *savedModelPredictor) Classify(ctx context.Context, examples []*predict.Example, context *predict.Example) ([][]predict.Class, predict.ModelInfo, error) {
modelInfo := predict.ModelInfo{
Name: ep.name,
Version: ep.version,
}
var contextBuf []byte
if context != nil {
var err error
contextBuf, err = ep.marshalExample(context)
if contextBuf != nil {
defer ep.putBuffer(contextBuf)
}
if err != nil {
return nil, modelInfo, err
}
}
serializedExamples := make([]string, len(examples))
for i, example := range examples {
buf, err := ep.marshalExample(example)
if err != nil {
return nil, modelInfo, err
}
if contextBuf != nil {
buf = append(buf, contextBuf...)
}
serializedExamples[i] = byteSlizeToString(buf)
defer ep.putBuffer(buf)
}
inputs, err := tf.NewTensor(serializedExamples)
if err != nil {
return nil, modelInfo, err
}
res, err := ep.runner.Run(map[string]*tf.Tensor{
"inputs": inputs,
}, nil)
if err != nil {
return nil, modelInfo, err
}
result := make([][]predict.Class, len(examples))
classesTensor, classesOk := res["classes"]
scoresTensor, scoresOk := res["scores"]
var classes [][]string
var scores [][]float32
var dims []int64
if scoresOk {
scores = scoresTensor.Value().([][]float32)
dims = scoresTensor.Shape()
}
if classesOk {
classes = classesTensor.Value().([][]string)
if dims == nil {
dims = classesTensor.Shape()
}
}
if dims != nil {
for exampleI := int64(0); exampleI < dims[0]; exampleI++ {
exampleClasses := make([]predict.Class, dims[1])
result[exampleI] = exampleClasses
for classI := int64(0); classI < dims[1]; classI++ {
if scoresOk {
exampleClasses[classI].Score = scores[exampleI][classI]
}
if classesOk {
exampleClasses[classI].Label = classes[exampleI][classI]
}
}
}
}
return result, modelInfo, err
}
func (ep *savedModelPredictor) Regress(ctx context.Context, examples []*predict.Example, context *predict.Example) ([]predict.Regression, predict.ModelInfo, error) {
modelInfo := predict.ModelInfo{
Name: ep.name,
Version: ep.version,
}
var contextBuf []byte
if context != nil {
var err error
contextBuf, err = ep.marshalExample(context)
if contextBuf != nil {
defer ep.putBuffer(contextBuf)
}
if err != nil {
return nil, modelInfo, err
}
}
serializedExamples := make([]string, len(examples))
for i, example := range examples {
buf, err := ep.marshalExample(example)
if err != nil {
return nil, modelInfo, err
}
if contextBuf != nil {
buf = append(buf, contextBuf...)
}
serializedExamples[i] = byteSlizeToString(buf)
defer ep.putBuffer(buf)
}
inputs, err := tf.NewTensor(serializedExamples)
if err != nil {
return nil, modelInfo, err
}
res, err := ep.runner.Run(map[string]*tf.Tensor{
"inputs": inputs,
}, nil)
if err != nil {
return nil, modelInfo, err
}
regressions := res["outputs"].Value().([][]float32)
results := make([]predict.Regression, len(regressions))
for i, reg := range regressions {
results[i].Value = reg[0]
}
return results, predict.ModelInfo{
Name: ep.name,
Version: ep.version,
}, nil
}
func (ep *savedModelPredictor) GetModelInfo(ctx context.Context) (predict.ModelInfo, error) {
return predict.ModelInfo{
Name: ep.name,
Version: ep.version,
}, nil
}
func (ep *savedModelPredictor) Close(ctx context.Context) error {
return ep.model.Session.Close()
}
type savedModelPredictorTensor struct {
t *tf.Tensor
}
func (ept *savedModelPredictorTensor) Value() interface{} {
return ept.t.Value()
}
func (ept *savedModelPredictorTensor) Shape() []int64 {
return ept.t.Shape()
}
func (ept *savedModelPredictorTensor) Type() predict.TensorType {
switch ept.t.DataType() {
case tf.Float:
return predict.TensorTypeFloat
case tf.Double:
return predict.TensorTypeDouble
case tf.Int32:
return predict.TensorTypeInt32
case tf.Uint32:
return predict.TensorTypeUInt32
case tf.String:
return predict.TensorTypeString
case tf.Int64:
return predict.TensorTypeInt64
case tf.Uint64:
return predict.TensorTypeUInt64
case tf.Bool:
return predict.TensorTypeBool
case tf.Complex64:
return predict.TensorTypeComplex64
case tf.Complex128:
return predict.TensorTypeComplex128
default:
panic(fmt.Errorf("unsupported type %v", ept.t.DataType()))
}
}
func byteSlizeToString(b []byte) string {
return *(*string)(unsafe.Pointer(&b)) // nolint: gas
}