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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | 1x 10x 10x 10x 10x 10x 2x 2x 17426x 6x 2x 6x 2x 6x 10x 10x 10x 10x 2x 2x 2x 119072x 119072x 119072x 119072x 119072x 119072x 119072x 119072x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x | import * as tfnode from "@tensorflow/tfjs-node"; import * as Jimp from "jimp"; import labelsMap from "./misc/labels_map.json"; const NUM_OF_CHANNELS = 3; enum EfficientnetCheckPoint { B0, B1, B2, B3, B4, B5, B6, B7, } class EfficientnetCheckPointFactory { static async create( checkPoint: EfficientnetCheckPoint ): Promise<EfficientnetModel> { switch (checkPoint) { case EfficientnetCheckPoint.B0: { const modelPath = "https://raw.githubusercontent.com/ntedgi/efficientnet/main/lib/tfjs/web_model/model.json"; const model = new EfficientnetModel(modelPath, 244); await model.load(); return model; } default: { throw Error(`${checkPoint} - Not Implemented Yet!`); } } } } interface Prediction { label: String precision: number } class EfficientnetResult { result: Prediction[] = [] constructor(values: Float32Array) { const arr = Array.from(values); const topValues = values.sort((a: number, b: number) => b - a).slice(0, 3); const indexes = topValues.map((e: number) => arr.indexOf(e)); const sum = topValues.reduce((a: number, b: number) => { return a + b; }, 0); indexes.forEach((value: number, index: number) => { // @ts-ignore this.result.push({label: labelsMap[value], precision: topValues[index] / sum * 100} as Prediction); }); } } class EfficientnetModel { modelPath: string; imageSize: number; model: any; constructor(modelPath: string, imageSize: number) { this.modelPath = modelPath; this.imageSize = imageSize; } async load() { const model = await tfnode.loadGraphModel(this.modelPath); this.model = model; } private async createTensor(image: any): Promise<any> { let values = new Float32Array( this.imageSize * this.imageSize * NUM_OF_CHANNELS ); let i = 0; image.scan( 0, 0, image.bitmap.width, image.bitmap.height, (x: number, y: number, idx: number) => { const pixel = Jimp.intToRGBA(image.getPixelColor(x, y)); pixel.r = ((pixel.r - 1) / 127.0) >> 0; pixel.g = ((pixel.g - 1) / 127.0) >> 0; pixel.b = ((pixel.b - 1) / 127.0) >> 0; values[i * NUM_OF_CHANNELS + 0] = pixel.r; values[i * NUM_OF_CHANNELS + 1] = pixel.g; values[i * NUM_OF_CHANNELS + 2] = pixel.b; i++; } ); const outShape = [this.imageSize, this.imageSize, NUM_OF_CHANNELS]; // @ts-ignore let imageTensor = tfnode.tensor3d(values, outShape, "float32"); imageTensor = imageTensor.expandDims(0); return imageTensor; } private async cropAndResize(image: any): Promise<any> { const width = image.bitmap.width; const height = image.bitmap.height; const cropPadding = 32; const paddedCenterCropSize = ((this.imageSize / (this.imageSize + cropPadding)) * Math.min(height, width)) >> 0; const offsetHeight = ((height - paddedCenterCropSize + 1) / 2) >> 0; const offsetWidth = (((width - paddedCenterCropSize + 1) / 2) >> 0) + 1; await image.crop( offsetWidth, offsetHeight, paddedCenterCropSize, paddedCenterCropSize ); await image.resize(this.imageSize, this.imageSize, Jimp.RESIZE_BICUBIC); return image; } private async predict(tensor: any): Promise<EfficientnetResult> { const objectArray = await this.model.predict(tensor); const values = objectArray.dataSync(); return new EfficientnetResult(values) } async inference(imgPath: string): Promise<EfficientnetResult> { let image = await Jimp.read(imgPath); image = await this.cropAndResize(image); const tensor = await this.createTensor(image); return this.predict(tensor); } } export { EfficientnetCheckPointFactory, EfficientnetCheckPoint, EfficientnetModel, EfficientnetResult } |