60 lines
2.0 KiB
TypeScript
60 lines
2.0 KiB
TypeScript
import * as tf from '../../dist/tfjs.esm';
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import { OutputLayerParams } from './types';
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function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
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const vec = tf.unstack(tf.transpose(x, [1, 0]));
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const sizes = [
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tf.sub(vec[2], vec[0]),
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tf.sub(vec[3], vec[1]),
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];
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const centers = [
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tf.add(vec[0], tf.div(sizes[0], 2)),
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tf.add(vec[1], tf.div(sizes[1], 2)),
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];
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return { sizes, centers };
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}
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function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
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const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);
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const vec = tf.unstack(tf.transpose(x1, [1, 0]));
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const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], 5)), sizes[0]), 2);
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const add0_out = tf.add(tf.mul(tf.div(vec[0], 10), sizes[0]), centers[0]);
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const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], 5)), sizes[1]), 2);
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const add1_out = tf.add(tf.mul(tf.div(vec[1], 10), sizes[1]), centers[1]);
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return tf.transpose(
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tf.stack([
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tf.sub(add0_out, div0_out),
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tf.sub(add1_out, div1_out),
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tf.add(add0_out, div0_out),
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tf.add(add1_out, div1_out),
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]),
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[1, 0],
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);
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}
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export function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams) {
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return tf.tidy(() => {
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const batchSize = boxPredictions.shape[0];
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let boxes = decodeBoxesLayer(
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tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,
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tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D,
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);
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boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
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const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
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let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor;
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scores = tf.reshape(scores, [batchSize, scores.shape[1] as number]);
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const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[];
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const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[];
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return { boxes: boxesByBatch, scores: scoresByBatch };
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});
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}
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