Javascript 让tensorflow在nodejs发球时更快？

我已经用tensorflow js创建了一个代码来检测图像上的对象，但是速度非常慢。 为此，我安装了npmjs软件包：

@tensorflow/tfjs
@tensorflow-models/coco-ssd
@tensorflow-models/mobilenet
get-image-data

这是我的剧本：

const tf = require('@tensorflow/tfjs')
// Load the binding (CPU computation)
const mobilenet = require('@tensorflow-models/mobilenet');
const cocoSsd = require("@tensorflow-models/coco-ssd");
const events = require('events');
const post_event = new events.EventEmitter();
const start = Date.now()
// for getting the data images
const image = require('get-image-data')

image('./img/cup.jpg',async(err, image)=>{
    const numChannels = 3;
    const numPixels = image.width * image.height;
    const values = new Int32Array(numPixels * numChannels);
    pixels = image.data
    for(let i = 0; i < numPixels; i++) {
        for (let channel = 0; channel < numChannels; ++channel) {
            values[i * numChannels + channel] = pixels[i * 4 + channel];
        }
    }
    const outShape = [image.height, image.width, numChannels];
    const input = tf.tensor3d(values, outShape, 'int32');
    await load(input)
});

const load=async img=>{
    console.log("IMG LOADED in ", (Date.now()-start)/1000,"s")
    let mobilenet_ = cocossd_ = false, post_predictions = []; 

    mobilenet.load().then(async model=>{
        console.log("mobilenet loaded in ",(Date.now()-start)/1000,"s")
        model.classify(img).then(async classify_predictions=>{
            for(i=0;i<classify_predictions.length;i++){
                const element = classify_predictions[i];
                const each_class = element["className"].split(", ")
                each_class.forEach(this_element=>{
                    post_predictions.push([this_element, (element.probability*100)]);
                })
            }
            post_event.emit("mobilenet")
        });        
    });

    cocoSsd.load().then(async model=>{
        console.log("cocossd loaded in ",(Date.now()-start)/1000,"s")
        model.detect(img).then(predictions=>{
            for(i=0;i<predictions.length;i++){
                const this_element = predictions[i];
                post_predictions.unshift([this_element.class, (this_element.score*100)]);
            }
            post_event.emit("cocossd")
        });
    })

    post_event.on("mobilenet", ()=>{
        console.log("mobilenet(longest) finished in ", (Date.now()-start)/1000,"s", post_predictions)
        mobilenet_=true
        if(mobilenet_ && cocossd_){
            post_event.emit("finish")
        }
    }).on("cocossd", ()=>{
        console.log("cocossd finished in ", (Date.now()-start)/1000,"s", post_predictions)
        cocossd_ = true
        if(mobilenet_ && cocossd_){
            post_event.emit("finish")
        }
    }).on("finish", ()=>{
        post_predictions.sort((a, b)=>{
            return b[1]-a[1];
        });
        console.log("Post in ", (Date.now()-start)/1000,"s", post_predictions)
    })
}

我看过一些视频，他们说nodejs版本的mobilenet需要20毫秒才能得到结果。但在我的应用程序上，需要10秒。也许我做错了什么。 有人能帮我解决这个问题吗

---

谢谢

加载模型需要一些时间。例如，您可以创建一个需要图像的express服务器并进行对象检测。启动服务器时，可以预加载模型。在每个api请求中，模型都已加载，检测在毫秒内完成（希望：-）

IMG LOADED in  0.486 s
cocossd loaded in  6.11 s
cocossd finished in  9.028 s [ [ 'cup', 95.68768739700317 ] ]
mobilenet loaded in  10.845 s
mobilenet(longest) finished in  12.795 s [
  [ 'cup', 95.68768739700317 ],
  [ 'cup', 69.30274367332458 ],
  [ 'espresso', 17.099112272262573 ],
  [ 'coffee mug', 13.384920358657837 ]
]
Post in  12.809 s [
  [ 'cup', 95.68768739700317 ],
  [ 'cup', 69.30274367332458 ],
  [ 'espresso', 17.099112272262573 ],
  [ 'coffee mug', 13.384920358657837 ]
]