Codex
271 lines
10 KiB
JavaScript
271 lines
10 KiB
JavaScript
import { dbToLinear, createScanPoints, distance3, findPeak, scanIndexToPosition, TAU } from "./math.js";
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import { binsForBand, extractStftBins, getFrameCount } from "./stft.js";
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function throwIfAborted(signal) {
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if (signal?.aborted) {
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const error = new Error("계산이 취소되었습니다.");
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error.name = "AbortError";
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throw error;
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}
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}
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function yieldToEventLoop() {
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return new Promise((resolve) => setTimeout(resolve, 0));
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}
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export function createDistanceMatrix(arrayDefinition, scan) {
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const points = createScanPoints(scan);
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const microphoneCount = arrayDefinition.microphones.length;
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const pointCount = scan.columns * scan.rows;
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const distances = new Float32Array(pointCount * microphoneCount);
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for (let pointIndex = 0; pointIndex < pointCount; pointIndex += 1) {
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const pointOffset = pointIndex * 4;
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const point = [points[pointOffset], points[pointOffset + 1], points[pointOffset + 2]];
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for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
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distances[pointIndex * microphoneCount + microphoneIndex] = distance3(
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point,
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arrayDefinition.microphones[microphoneIndex].position,
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);
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}
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}
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return distances;
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}
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export async function computeBinMap(options) {
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const {
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bin,
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spectrum,
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frameCount,
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arrayDefinition,
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pointCount,
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distances,
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sampleRate,
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fftSize,
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speedOfSound,
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signal,
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pointBatchSize,
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} = options;
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const microphoneCount = arrayDefinition.microphones.length;
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const output = new Float32Array(pointCount);
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const steeringReal = new Float64Array(microphoneCount);
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const steeringImaginary = new Float64Array(microphoneCount);
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const angularWaveNumber = (TAU * bin * sampleRate) / (fftSize * speedOfSound);
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const microphoneScale = 1 / microphoneCount;
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for (let pointIndex = 0; pointIndex < pointCount; pointIndex += 1) {
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throwIfAborted(signal);
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const distanceOffset = pointIndex * microphoneCount;
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for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
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const microphone = arrayDefinition.microphones[microphoneIndex];
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const calibrationPhase = ((microphone.phaseOffsetDeg ?? 0) * Math.PI) / 180;
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const phase = angularWaveNumber * distances[distanceOffset + microphoneIndex] - calibrationPhase;
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const calibrationGain = dbToLinear(-(microphone.sensitivityDb ?? 0));
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steeringReal[microphoneIndex] = Math.cos(phase) * calibrationGain;
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steeringImaginary[microphoneIndex] = Math.sin(phase) * calibrationGain;
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}
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let accumulatedPower = 0;
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for (let frameIndex = 0; frameIndex < frameCount; frameIndex += 1) {
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const spectrumOffset = frameIndex * microphoneCount;
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let sumReal = 0;
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let sumImaginary = 0;
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for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
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const spectrumIndex = spectrumOffset + microphoneIndex;
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const inputReal = spectrum.real[spectrumIndex];
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const inputImaginary = spectrum.imaginary[spectrumIndex];
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const phaseReal = steeringReal[microphoneIndex];
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const phaseImaginary = steeringImaginary[microphoneIndex];
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sumReal += inputReal * phaseReal - inputImaginary * phaseImaginary;
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sumImaginary += inputReal * phaseImaginary + inputImaginary * phaseReal;
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}
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accumulatedPower += (sumReal * sumReal + sumImaginary * sumImaginary) * microphoneScale * microphoneScale;
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}
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output[pointIndex] = accumulatedPower / frameCount;
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if ((pointIndex + 1) % pointBatchSize === 0) await yieldToEventLoop();
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}
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return output;
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}
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export function estimateDasWorkload(options) {
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const { channels, sampleRate, arrayDefinition, experiment } = options;
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const { fftSize, hopSize } = experiment.stft;
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const bandBins = experiment.frequencyBands.map((band) => binsForBand(band, sampleRate, fftSize));
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const selectedBins = [...new Set(bandBins.flat())].sort((a, b) => a - b);
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const frameCount = getFrameCount(channels[0].length, fftSize, hopSize);
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const microphoneCount = arrayDefinition.microphones.length;
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const pointCount = experiment.scan.columns * experiment.scan.rows;
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const bandCount = experiment.frequencyBands.length;
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const distanceBytes = pointCount * microphoneCount * Float32Array.BYTES_PER_ELEMENT;
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const outputBytes = bandCount * pointCount * Float32Array.BYTES_PER_ELEMENT * 2;
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const binMapBytes = pointCount * Float32Array.BYTES_PER_ELEMENT;
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const spectrumBytesPerBin = frameCount * microphoneCount * Float32Array.BYTES_PER_ELEMENT * 2;
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const fftWorkspaceBytes = fftSize * Float64Array.BYTES_PER_ELEMENT * 2;
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const baseWorkingBytes = distanceBytes + outputBytes + binMapBytes + fftWorkspaceBytes;
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const complexMacCount = pointCount * microphoneCount * frameCount * selectedBins.length;
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return {
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bandBins,
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selectedBins,
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frameCount,
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microphoneCount,
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pointCount,
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distanceBytes,
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outputBytes,
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spectrumBytesPerBin,
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baseWorkingBytes,
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complexMacCount,
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};
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}
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export async function computeDasMap(options) {
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const {
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channels,
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sampleRate,
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arrayDefinition,
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scene,
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experiment,
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signal,
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onProgress,
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maximumWorkingBytes = 512 * 1024 * 1024,
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maximumComplexMacs = 1_000_000_000,
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maximumFftButterflies = 1_000_000_000,
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maximumSpectrumBatchBins = Number.MAX_SAFE_INTEGER,
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pointBatchSize = 64,
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stftTransformBatchSize = 32,
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} = options;
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if (channels.length !== arrayDefinition.microphones.length) {
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throw new Error(`채널 수 ${channels.length}와 마이크 수 ${arrayDefinition.microphones.length}가 다릅니다.`);
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}
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if (!Number.isInteger(pointBatchSize) || pointBatchSize < 1) throw new Error("pointBatchSize는 1 이상의 정수여야 합니다.");
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const workload = estimateDasWorkload({ channels, sampleRate, arrayDefinition, experiment });
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if (workload.complexMacCount > maximumComplexMacs) {
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throw new Error(
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`CPU DAS 예상 복소 누산 ${workload.complexMacCount.toLocaleString()}회가 ` +
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`현재 한도 ${maximumComplexMacs.toLocaleString()}회를 넘습니다. grid, duration 또는 대역폭을 줄이세요.`,
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);
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}
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const availableSpectrumBytes = maximumWorkingBytes - workload.baseWorkingBytes;
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const batchCapacity = Math.floor(availableSpectrumBytes / workload.spectrumBytesPerBin);
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if (batchCapacity < 1) {
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throw new Error(
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`CPU DAS 기본 working set ${(workload.baseWorkingBytes / 1024 ** 2).toFixed(1)} MiB가 ` +
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`현재 한도 ${(maximumWorkingBytes / 1024 ** 2).toFixed(1)} MiB를 넘습니다.`,
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);
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}
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const batchSize = Math.max(
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1,
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Math.min(workload.selectedBins.length, batchCapacity, Math.max(1, maximumSpectrumBatchBins)),
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);
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const spectrumBatchCount = Math.ceil(workload.selectedBins.length / batchSize);
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const fftButterflyCount =
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spectrumBatchCount *
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workload.frameCount *
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workload.microphoneCount *
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(experiment.stft.fftSize / 2) *
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Math.log2(experiment.stft.fftSize);
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if (fftButterflyCount > maximumFftButterflies) {
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throw new Error(
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`CPU STFT 예상 butterfly ${Math.ceil(fftButterflyCount).toLocaleString()}회가 ` +
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`현재 한도 ${maximumFftButterflies.toLocaleString()}회를 넘습니다. duration, FFT 크기 또는 대역폭을 줄이세요.`,
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);
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}
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const distances = createDistanceMatrix(arrayDefinition, experiment.scan);
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const rawMaps = experiment.frequencyBands.map(() => new Float32Array(workload.pointCount));
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const bandIndexesByBin = new Map();
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workload.bandBins.forEach((bins, bandIndex) => {
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bins.forEach((bin) => {
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if (!bandIndexesByBin.has(bin)) bandIndexesByBin.set(bin, []);
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bandIndexesByBin.get(bin).push(bandIndex);
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});
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});
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let completedBins = 0;
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for (let batchStart = 0; batchStart < workload.selectedBins.length; batchStart += batchSize) {
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throwIfAborted(signal);
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await yieldToEventLoop();
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throwIfAborted(signal);
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const batchBins = workload.selectedBins.slice(batchStart, batchStart + batchSize);
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const stft = await extractStftBins(channels, {
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fftSize: experiment.stft.fftSize,
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hopSize: experiment.stft.hopSize,
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bins: batchBins,
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signal,
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transformBatchSize: stftTransformBatchSize,
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onProgress: ({ completedTransforms, totalTransforms }) =>
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onProgress?.({
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stage: "stft",
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completedBins,
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totalBins: workload.selectedBins.length,
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completedTransforms,
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totalTransforms,
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spectrumBatch: Math.floor(batchStart / batchSize) + 1,
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spectrumBatchCount,
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}),
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});
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await yieldToEventLoop();
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throwIfAborted(signal);
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for (const bin of batchBins) {
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const binMap = await computeBinMap({
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bin,
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spectrum: stft.spectra.get(bin),
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frameCount: stft.frameCount,
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arrayDefinition,
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pointCount: workload.pointCount,
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distances,
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sampleRate,
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fftSize: experiment.stft.fftSize,
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speedOfSound: scene.speedOfSound,
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signal,
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pointBatchSize,
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});
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for (const bandIndex of bandIndexesByBin.get(bin)) {
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const rawMap = rawMaps[bandIndex];
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for (let pointIndex = 0; pointIndex < workload.pointCount; pointIndex += 1) {
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rawMap[pointIndex] += binMap[pointIndex];
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}
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}
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completedBins += 1;
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onProgress?.({ stage: "beamforming", completedBins, totalBins: workload.selectedBins.length });
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}
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}
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const bands = experiment.frequencyBands.map((band, bandIndex) => {
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const rawMap = rawMaps[bandIndex];
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for (let pointIndex = 0; pointIndex < workload.pointCount; pointIndex += 1) {
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rawMap[pointIndex] /= workload.bandBins[bandIndex].length;
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}
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const rawPeak = findPeak(rawMap);
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const displayMap = Float32Array.from(rawMap);
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let displayScale = "power";
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if (experiment.beamforming.normalization === "peak" && rawPeak.value > 0) {
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displayScale = "normalized";
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for (let pointIndex = 0; pointIndex < workload.pointCount; pointIndex += 1) {
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displayMap[pointIndex] /= rawPeak.value;
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}
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}
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return {
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id: band.id,
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color: band.color,
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bins: workload.bandBins[bandIndex],
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rawMap,
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displayMap,
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displayScale,
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peak: {
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index: rawPeak.index,
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position: scanIndexToPosition(rawPeak.index, experiment.scan),
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rawPower: rawPeak.value,
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},
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};
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});
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return {
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backend: "cpu",
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frameCount: workload.frameCount,
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microphoneCount: workload.microphoneCount,
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pointCount: workload.pointCount,
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workload,
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fftButterflyCount,
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bands,
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};
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}
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