Google launches TensorFlow Lite 1.0 for mobile and embedded devices | VentureBeat
Google today introduced TensorFlow Lite 1.0 , its framework for developers deploying AI models on mo...
https://venturebeat.com/2019/03/06/google-launches-tensorflow-lite-1-0-for-mobile-and-embeddable-devices/
TensorFlow Lite 1.0 がリリースされた、このめでたい日に TensorFlow にプレゼントを送ろうと思って作りました。
TensorFlow Lite を Go 言語から扱えるパッケージです。なるべく C の API に忠実に実装したので Go 言語感がない部分もあるかもしれませんが、それはこれからです。
GitHub - mattn/go-tflite
go-tflite Go binding for TensorFlow Lite Usage See _example/main.go Requirements TensorFlow Lite Ins...
https://github.com/mattn/go-tflite
使い方は TensorFlow Lite でプログラミングした事がある方なら分かるはずです。
package main
import (
"bufio"
"flag"
"fmt"
"image"
_ "image/png"
"log"
"os"
"sort"
"github.com/mattn/go-tflite"
"github.com/nfnt/resize"
)
func loadLabels(filename string) ([]string, error) {
labels := []string{}
f, err := os.Open("labels.txt")
if err != nil {
return nil, err
}
defer f.Close()
scanner := bufio.NewScanner(f)
for scanner.Scan() {
labels = append(labels, scanner.Text())
}
return labels, nil
}
func main() {
var model_path, label_path, image_path string
flag.StringVar(&model_path, "model", "mobilenet_quant_v1_224.tflite", "path to model file")
flag.StringVar(&label_path, "label", "labels.txt", "path to label file")
flag.StringVar(&image_path, "image", "grace_hopper.png", "path to image file")
flag.Parse()
f, err := os.Open(image_path)
if err != nil {
log.Fatal(err)
}
defer f.Close()
img, _, err := image.Decode(f)
if err != nil {
log.Fatal(err)
}
labels, err := loadLabels(label_path)
if err != nil {
log.Fatal(err)
}
model := tflite.NewModelFromFile(model_path)
if model == nil {
log.Fatal("cannot load model")
}
defer model.Delete()
options := tflite.NewInterpreterOptions()
options.SetNumThread(4)
interpreter := tflite.NewInterpreter(model, options)
if interpreter == nil {
log.Fatal("cannot create interpreter")
}
defer interpreter.Delete()
status := interpreter.AllocateTensors()
if status != tflite.OK {
log.Fatal("allocate failed")
}
input := interpreter.GetInputTensor(0)
wanted_height := input.Dim(1)
wanted_width := input.Dim(2)
wanted_channels := input.Dim(3)
wanted_type := input.Type()
fmt.Println(wanted_height, wanted_width, wanted_channels, wanted_type)
resized := resize.Resize(uint(wanted_width), uint(wanted_height), img, resize.NearestNeighbor)
bounds := resized.Bounds()
dx, dy := bounds.Dx(), bounds.Dy()
if wanted_type == tflite.UInt8 {
bb := make([]byte, dx*dy*wanted_channels)
for y := 0; y < dy; y++ {
for x := 0; x < dx; x++ {
col := resized.At(x, y)
r, g, b, _ := col.RGBA()
bb[(y*dx+x)*3+0] = byte(float64(r) / 255.0)
bb[(y*dx+x)*3+1] = byte(float64(g) / 255.0)
bb[(y*dx+x)*3+2] = byte(float64(b) / 255.0)
}
}
input.CopyFromBuffer(bb)
} else {
log.Fatal("is not wanted type")
}
status = interpreter.Invoke()
if status != tflite.OK {
log.Fatal("invoke failed")
}
output := interpreter.GetOutputTensor(0)
output_size := output.Dim(output.NumDims() - 1)
b := make([]byte, output_size)
type result struct {
score float64
index int
}
status = output.CopyToBuffer(b)
if status != tflite.OK {
log.Fatal("output failed")
}
results := []result{}
for i := 0; i < output_size; i++ {
score := float64(b[i]) / 255.0
if score < 0.2 {
continue
}
results = append(results, result{score: score, index: i})
}
sort.Slice(results, func(i, j int) bool {
return results[i].score > results[j].score
})
for i := 0; i < len(results); i++ {
fmt.Printf("%02d: %s: %f\n", results[i].index, labels[results[i].index], results[i].score)
if i > 5 {
break
}
}
}
label_image でおなじみの grace_hopper も正しく動きます。
一応、TensorFlow Lite の C API は全て移植したつもりですが、何かバグっていたら教えて下さい。
