retinanetjs
Version:
Wrapper for models built using keras-retinanet.
183 lines • 16.8 kB
JavaScript
import * as tf from '@tensorflow/tfjs';
import { Zeros } from '@tensorflow/tfjs-layers/dist/initializers'; // tslint:disable-line
import { anchorsForShape, defaultAnchorParameters } from './anchors';
class UpsampleLike extends tf.layers.Layer {
constructor() {
super({});
}
computeOutputShape(inputShape) {
return [
inputShape[0][0],
inputShape[1][1],
inputShape[1][2],
inputShape[0][3]
];
}
call(inputs, _) {
const [source, target] = inputs;
const targetShape = target.shape;
return tf.image.resizeNearestNeighbor(source, [
targetShape[1],
targetShape[2]
]);
}
static get className() {
return 'UpsampleLike';
}
}
class PriorProbability extends Zeros {
}
// tslint:disable-line
/** @nocollapse */
PriorProbability.className = 'PriorProbability';
tf.serialization.registerClass(UpsampleLike); // Needed for serialization.
tf.serialization.registerClass(PriorProbability);
/**
* Represents a RetinaNet model. Rather than creating directly,
* it is intended to be created using `load()`.
*/
export class RetinaNet {
constructor(model, classes, preprocessingMode, anchorParams = defaultAnchorParameters) {
const [height, width] = model.inputs[0].shape.slice(1, 3);
// tslint:disable-next-line
if (height === -1 || width === -1) {
throw new Error('RetinaNetJS only supports fixed input sizes.');
}
// tslint:disable-next-line
if (preprocessingMode !== 'tf' && preprocessingMode !== 'caffe') {
throw new Error('preprocessingMode must be either `tf` or `caffe`.');
}
this.width = width;
this.height = height;
this.model = model;
this.classes = classes;
this.preprocessingMode = preprocessingMode;
this.anchorParams = anchorParams;
}
/**
* Computes predictions. We currently do not support class-specific filtering.
* When non-max suppression is applied, it will be across all boxes, regardless of class.
*
* @param img The image object on which to run object detection
* @param threshold The prediction threshold
* @param nmsThreshold The non-max suppresion IoU threshold
*/
async detect(img, threshold = 0.5, nmsThreshold = 0.5) {
// Build model input from image
const [X, padX, padY] = tf.tidy(() => {
const imageTensor = !(img instanceof tf.Tensor)
? tf.browser.fromPixels(img)
: img;
return this.handleImageTensor(imageTensor);
});
// Run inference
const y = this.model.predict(X);
const [coords, classification] = tf.tidy(() => {
const [boxDeltas, classScores] = y.map(t => t.squeeze([0]));
const anchorBoxes = anchorsForShape(this.model.inputs[0].shape.slice(1, 3), this.anchorParams);
const mean = [0, 0, 0, 0];
const std = [0.2, 0.2, 0.2, 0.2];
const width = anchorBoxes
.slice([0, 2], [-1, 1])
.sub(anchorBoxes.slice([0, 0], [-1, 1]));
const height = anchorBoxes
.slice([0, 2], [-1, 1])
.sub(anchorBoxes.slice([0, 0], [-1, 1]));
const x1y1x2y2 = tf.concat([0, 1, 2, 3].map(i => {
return anchorBoxes.slice([0, i], [-1, 1]).add(boxDeltas
.slice([0, i], [-1, 1])
.mul(std[i])
.add(mean[i])
.mul(i % 2 === 0 ? width : height));
}), 1);
// TODO: Add support for class-specific filtering and turning nms on and off, just
// like in retinanet.
return [x1y1x2y2, classScores];
});
const selected = await tf.image.nonMaxSuppressionAsync(coords, classification.max(1, false), 300, nmsThreshold, threshold);
const detections = tf.tidy(() => {
const classificationNms = classification.gather(selected);
const coordsNms = coords
.gather(selected)
.div([
this.width - padX,
this.height - padY,
this.width - padX,
this.height - padY
]);
const x1y1x2y2ls = tf
.concat([
coordsNms,
classificationNms
.argMax(1)
.expandDims(1)
.cast('float32'),
classificationNms.max(1, true)
], 1)
.arraySync();
return x1y1x2y2ls.map(r => {
const [x1, y1, x2, y2, labelIdx, score] = r;
return { label: this.classes[labelIdx], score, x1, x2, y1, y2 };
});
});
X.dispose();
y.map(t => t.dispose());
selected.dispose();
return detections;
}
/**
* Remove the model from memory.
*/
dispose() {
this.model.dispose();
}
handleImageTensor(imageTensor) {
return tf.tidy(() => {
const inputHeight = imageTensor.shape[0];
const inputWidth = imageTensor.shape[1];
const [outputHeight, outputWidth] = [this.height, this.width];
const scale = Math.min(outputHeight / inputHeight, outputWidth / inputWidth);
const padY = outputHeight - Math.round(scale * inputHeight);
const padX = outputWidth - Math.round(scale * inputWidth);
const scaledTensor = scale === 1
? imageTensor
: imageTensor.resizeBilinear([
Math.round(scale * inputHeight),
Math.round(scale * inputWidth)
]);
const paddedTensor = padX === 0 && padY === 0
? scaledTensor
: scaledTensor.pad([[0, padY], [0, padX], [0, 0]]);
const normedTensor = this.preprocessingMode === 'tf'
? paddedTensor.sub(127.5).div(127.5)
: paddedTensor.sub(tf.tensor3d([[[103.939, 116.779, 123.68]]]));
return [normedTensor.expandDims(0).cast('float32'), padX, padY];
});
}
}
/**
*
* @param modelPath The path to the model or a `tf.io.IOHandler` object
* @param classes The list of detected classes
* @param preprocessingMode One of `tf` or `caffe`. Check the `preprocess_images`
* method of your backbone to see which you should use.
* @param onProgress A callback to report progress
* @param anchorParams The anchor parameters for your model
*/
export async function load(modelPath, classes, preprocessingMode, onProgress, anchorParams = defaultAnchorParameters) {
const tfOnProgress = (progress) => onProgress(0.9 * progress, 'Downloading');
const model = await tf.loadLayersModel(modelPath, {
onProgress: tfOnProgress,
strict: false
});
const detector = new RetinaNet(model, classes, preprocessingMode, anchorParams);
if (onProgress)
onProgress(0.92, 'Building'); // tslint:disable-line
setTimeout(async () => {
await detector.detect(tf.ones([100, 100, 3]));
if (onProgress)
onProgress(1.0, 'Finished'); // tslint:disable-line
}, 100);
return detector;
}
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