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3 Commits
23917b2976
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1ff71ab241
| Author | SHA1 | Date | |
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1ff71ab241
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cf7a285f57
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79a37d927a
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@ -63,18 +63,6 @@ export async function snapshotScheduleExists(sql: Psql, id: number) {
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return rows.length > 0;
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}
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export async function videoHasActiveSchedule(sql: Psql, aid: number) {
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const rows = await sql<{ status: string }[]>`
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SELECT status
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FROM snapshot_schedule
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WHERE aid = ${aid}
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AND (status = 'pending'
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OR status = 'processing'
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)
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`
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return rows.length > 0;
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}
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export async function videoHasActiveScheduleWithType(sql: Psql, aid: number, type: string) {
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const rows = await sql<{ status: string }[]>`
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SELECT status FROM snapshot_schedule
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@ -292,23 +280,23 @@ export async function adjustSnapshotTime(
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}
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export async function getSnapshotsInNextSecond(sql: Psql) {
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const rows = await sql<SnapshotScheduleType[]>`
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return sql<SnapshotScheduleType[]>`
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SELECT *
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FROM snapshot_schedule
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WHERE started_at <= NOW() + INTERVAL '1 seconds' AND status = 'pending' AND type != 'normal'
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ORDER BY
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CASE
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WHERE started_at <= NOW() + INTERVAL '1 seconds'
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AND status = 'pending'
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AND type != 'normal'
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ORDER BY CASE
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WHEN type = 'milestone' THEN 0
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ELSE 1
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END,
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started_at
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LIMIT 10;
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`
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return rows;
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`;
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}
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export async function getBulkSnapshotsInNextSecond(sql: Psql) {
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const rows = await sql<SnapshotScheduleType[]>`
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return sql<SnapshotScheduleType[]>`
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SELECT *
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FROM snapshot_schedule
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WHERE (started_at <= NOW() + INTERVAL '15 seconds')
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@ -320,27 +308,33 @@ export async function getBulkSnapshotsInNextSecond(sql: Psql) {
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END,
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started_at
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LIMIT 1000;
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`
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return rows;
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`;
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}
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export async function setSnapshotStatus(sql: Psql, id: number, status: string) {
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return await sql`
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UPDATE snapshot_schedule SET status = ${status} WHERE id = ${id}
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return sql`
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UPDATE snapshot_schedule
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SET status = ${status}
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WHERE id = ${id}
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`;
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}
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export async function bulkSetSnapshotStatus(sql: Psql, ids: number[], status: string) {
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return await sql`
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UPDATE snapshot_schedule SET status = ${status} WHERE id = ANY(${ids})
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return sql`
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UPDATE snapshot_schedule
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SET status = ${status}
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WHERE id = ANY (${ids})
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`;
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}
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export async function getVideosWithoutActiveSnapshotSchedule(sql: Psql) {
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export async function getVideosWithoutActiveSnapshotScheduleByType(sql: Psql, type: string) {
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const rows = await sql<{ aid: string }[]>`
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SELECT s.aid
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FROM songs s
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LEFT JOIN snapshot_schedule ss ON s.aid = ss.aid AND (ss.status = 'pending' OR ss.status = 'processing')
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LEFT JOIN snapshot_schedule ss ON
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s.aid = ss.aid AND
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(ss.status = 'pending' OR ss.status = 'processing') AND
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ss.type = ${type}
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WHERE ss.aid IS NULL
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`;
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return rows.map((r) => Number(r.aid));
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@ -1,179 +0,0 @@
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import { AutoTokenizer, PreTrainedTokenizer } from "@huggingface/transformers";
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import * as ort from "onnxruntime";
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function softmax(logits: Float32Array): number[] {
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const maxLogit = Math.max(...logits);
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const exponents = logits.map((logit) => Math.exp(logit - maxLogit));
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const sumOfExponents = exponents.reduce((sum, exp) => sum + exp, 0);
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return Array.from(exponents.map((exp) => exp / sumOfExponents));
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}
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// 配置参数
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const sentenceTransformerModelName = "alikia2x/jina-embedding-v3-m2v-1024";
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const onnxClassifierPath = "./model/video_classifier_v3_17.onnx";
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const onnxEmbeddingPath = "./model/embedding_original.onnx";
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const testDataPath = "./data/filter/test1.jsonl";
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// 初始化会话
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const [sessionClassifier, sessionEmbedding] = await Promise.all([
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ort.InferenceSession.create(onnxClassifierPath),
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ort.InferenceSession.create(onnxEmbeddingPath),
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]);
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let tokenizer: PreTrainedTokenizer;
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// 初始化分词器
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async function loadTokenizer() {
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const tokenizerConfig = { local_files_only: true };
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tokenizer = await AutoTokenizer.from_pretrained(sentenceTransformerModelName, tokenizerConfig);
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}
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// 新的嵌入生成函数（使用ONNX）
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async function getONNXEmbeddings(texts: string[], session: ort.InferenceSession): Promise<number[]> {
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const { input_ids } = await tokenizer(texts, {
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add_special_tokens: false,
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return_tensor: false,
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});
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// 构造输入参数
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const cumsum = (arr: number[]): number[] =>
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arr.reduce((acc: number[], num: number, i: number) => [...acc, num + (acc[i - 1] || 0)], []);
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const offsets: number[] = [0, ...cumsum(input_ids.slice(0, -1).map((x: string) => x.length))];
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const flattened_input_ids = input_ids.flat();
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// 准备ONNX输入
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const inputs = {
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input_ids: new ort.Tensor("int64", new BigInt64Array(flattened_input_ids.map(BigInt)), [
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flattened_input_ids.length,
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]),
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offsets: new ort.Tensor("int64", new BigInt64Array(offsets.map(BigInt)), [offsets.length]),
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};
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// 执行推理
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const { embeddings } = await session.run(inputs);
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return Array.from(embeddings.data as Float32Array);
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}
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// 分类推理函数
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async function runClassification(embeddings: number[]): Promise<number[]> {
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const inputTensor = new ort.Tensor(
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Float32Array.from(embeddings),
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[1, 3, 1024],
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);
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const { logits } = await sessionClassifier.run({ channel_features: inputTensor });
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return softmax(logits.data as Float32Array);
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}
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// 指标计算函数
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function calculateMetrics(labels: number[], predictions: number[], elapsedTime: number): {
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accuracy: number;
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precision: number;
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recall: number;
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f1: number;
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"Class 0 Prec": number;
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speed: string;
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} {
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// 输出label和prediction不一样的index列表
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const arr = [];
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for (let i = 0; i < labels.length; i++) {
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if (labels[i] !== predictions[i] && predictions[i] == 0) {
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arr.push([i + 1, labels[i], predictions[i]]);
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}
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}
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console.log(arr);
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// 初始化混淆矩阵
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const classCount = Math.max(...labels, ...predictions) + 1;
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const matrix = Array.from({ length: classCount }, () => Array.from({ length: classCount }, () => 0));
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// 填充矩阵
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labels.forEach((trueLabel, i) => {
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matrix[trueLabel][predictions[i]]++;
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});
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// 计算各指标
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let totalTP = 0, totalFP = 0, totalFN = 0;
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for (let c = 0; c < classCount; c++) {
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const TP = matrix[c][c];
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const FP = matrix.flatMap((row, i) => i === c ? [] : [row[c]]).reduce((a, b) => a + b, 0);
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const FN = matrix[c].filter((_, i) => i !== c).reduce((a, b) => a + b, 0);
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totalTP += TP;
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totalFP += FP;
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totalFN += FN;
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}
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const precision = totalTP / (totalTP + totalFP);
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const recall = totalTP / (totalTP + totalFN);
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const f1 = 2 * (precision * recall) / (precision + recall) || 0;
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// 计算Class 0 Precision
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const class0TP = matrix[0][0];
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const class0FP = matrix.flatMap((row, i) => i === 0 ? [] : [row[0]]).reduce((a, b) => a + b, 0);
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const class0Precision = class0TP / (class0TP + class0FP) || 0;
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return {
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accuracy: labels.filter((l, i) => l === predictions[i]).length / labels.length,
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precision,
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recall,
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f1,
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speed: `${(labels.length / (elapsedTime / 1000)).toFixed(1)} samples/sec`,
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"Class 0 Prec": class0Precision,
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};
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}
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// 改造后的评估函数
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async function evaluateModel(session: ort.InferenceSession): Promise<{
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accuracy: number;
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precision: number;
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recall: number;
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f1: number;
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"Class 0 Prec": number;
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}> {
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const data = await Deno.readTextFile(testDataPath);
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const samples = data.split("\n")
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.map((line) => {
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try {
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return JSON.parse(line);
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} catch {
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return null;
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}
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})
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.filter(Boolean);
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const allPredictions: number[] = [];
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const allLabels: number[] = [];
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const t = new Date().getTime();
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for (const sample of samples) {
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try {
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const embeddings = await getONNXEmbeddings([
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sample.title,
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sample.description,
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sample.tags.join(","),
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], session);
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const probabilities = await runClassification(embeddings);
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allPredictions.push(probabilities.indexOf(Math.max(...probabilities)));
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allLabels.push(sample.label);
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} catch (error) {
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console.error("Processing error:", error);
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}
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}
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const elapsed = new Date().getTime() - t;
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return calculateMetrics(allLabels, allPredictions, elapsed);
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}
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// 主函数
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async function main() {
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await loadTokenizer();
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const metrics = await evaluateModel(sessionEmbedding);
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console.log("Model Metrics:");
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console.table(metrics);
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}
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await main();
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@ -1,171 +0,0 @@
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import { AutoTokenizer, PreTrainedTokenizer } from "@huggingface/transformers";
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import * as ort from "onnxruntime";
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function softmax(logits: Float32Array): number[] {
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const maxLogit = Math.max(...logits);
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const exponents = logits.map((logit) => Math.exp(logit - maxLogit));
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const sumOfExponents = exponents.reduce((sum, exp) => sum + exp, 0);
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return Array.from(exponents.map((exp) => exp / sumOfExponents));
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}
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// 配置参数
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const sentenceTransformerModelName = "alikia2x/jina-embedding-v3-m2v-1024";
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const onnxClassifierPath = "./model/video_classifier_v3_11.onnx";
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const onnxEmbeddingOriginalPath = "./model/embedding_original.onnx";
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const onnxEmbeddingQuantizedPath = "./model/embedding_original.onnx";
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// 初始化会话
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const [sessionClassifier, sessionEmbeddingOriginal, sessionEmbeddingQuantized] = await Promise.all([
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ort.InferenceSession.create(onnxClassifierPath),
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ort.InferenceSession.create(onnxEmbeddingOriginalPath),
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ort.InferenceSession.create(onnxEmbeddingQuantizedPath),
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]);
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let tokenizer: PreTrainedTokenizer;
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// 初始化分词器
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async function loadTokenizer() {
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const tokenizerConfig = { local_files_only: true };
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tokenizer = await AutoTokenizer.from_pretrained(sentenceTransformerModelName, tokenizerConfig);
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}
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|
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// 新的嵌入生成函数（使用ONNX）
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async function getONNXEmbeddings(texts: string[], session: ort.InferenceSession): Promise<number[]> {
|
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const { input_ids } = await tokenizer(texts, {
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add_special_tokens: false,
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return_tensor: false,
|
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});
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|
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// 构造输入参数
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const cumsum = (arr: number[]): number[] =>
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arr.reduce((acc: number[], num: number, i: number) => [...acc, num + (acc[i - 1] || 0)], []);
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const offsets: number[] = [0, ...cumsum(input_ids.slice(0, -1).map((x: string) => x.length))];
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const flattened_input_ids = input_ids.flat();
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// 准备ONNX输入
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const inputs = {
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input_ids: new ort.Tensor("int64", new BigInt64Array(flattened_input_ids.map(BigInt)), [
|
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flattened_input_ids.length,
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]),
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offsets: new ort.Tensor("int64", new BigInt64Array(offsets.map(BigInt)), [offsets.length]),
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};
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// 执行推理
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const { embeddings } = await session.run(inputs);
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return Array.from(embeddings.data as Float32Array);
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}
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// 分类推理函数
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async function runClassification(embeddings: number[]): Promise<number[]> {
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const inputTensor = new ort.Tensor(
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Float32Array.from(embeddings),
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[1, 4, 1024],
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);
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const { logits } = await sessionClassifier.run({ channel_features: inputTensor });
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return softmax(logits.data as Float32Array);
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}
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// 指标计算函数
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function calculateMetrics(labels: number[], predictions: number[], elapsedTime: number): {
|
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accuracy: number;
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precision: number;
|
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recall: number;
|
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f1: number;
|
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speed: string;
|
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} {
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// 初始化混淆矩阵
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const classCount = Math.max(...labels, ...predictions) + 1;
|
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const matrix = Array.from({ length: classCount }, () => Array.from({ length: classCount }, () => 0));
|
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|
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// 填充矩阵
|
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labels.forEach((trueLabel, i) => {
|
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matrix[trueLabel][predictions[i]]++;
|
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});
|
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|
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// 计算各指标
|
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let totalTP = 0, totalFP = 0, totalFN = 0;
|
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|
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for (let c = 0; c < classCount; c++) {
|
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const TP = matrix[c][c];
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const FP = matrix.flatMap((row, i) => i === c ? [] : [row[c]]).reduce((a, b) => a + b, 0);
|
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const FN = matrix[c].filter((_, i) => i !== c).reduce((a, b) => a + b, 0);
|
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|
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totalTP += TP;
|
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totalFP += FP;
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totalFN += FN;
|
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}
|
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|
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const precision = totalTP / (totalTP + totalFP);
|
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const recall = totalTP / (totalTP + totalFN);
|
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const f1 = 2 * (precision * recall) / (precision + recall) || 0;
|
||||
|
||||
return {
|
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accuracy: labels.filter((l, i) => l === predictions[i]).length / labels.length,
|
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precision,
|
||||
recall,
|
||||
f1,
|
||||
speed: `${(labels.length / (elapsedTime / 1000)).toFixed(1)} samples/sec`,
|
||||
};
|
||||
}
|
||||
|
||||
// 改造后的评估函数
|
||||
async function evaluateModel(session: ort.InferenceSession): Promise<{
|
||||
accuracy: number;
|
||||
precision: number;
|
||||
recall: number;
|
||||
f1: number;
|
||||
}> {
|
||||
const data = await Deno.readTextFile("./data/filter/test1.jsonl");
|
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const samples = data.split("\n")
|
||||
.map((line) => {
|
||||
try {
|
||||
return JSON.parse(line);
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
})
|
||||
.filter(Boolean);
|
||||
|
||||
const allPredictions: number[] = [];
|
||||
const allLabels: number[] = [];
|
||||
|
||||
const t = new Date().getTime();
|
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for (const sample of samples) {
|
||||
try {
|
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const embeddings = await getONNXEmbeddings([
|
||||
sample.title,
|
||||
sample.description,
|
||||
sample.tags.join(","),
|
||||
sample.author_info,
|
||||
], session);
|
||||
|
||||
const probabilities = await runClassification(embeddings);
|
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allPredictions.push(probabilities.indexOf(Math.max(...probabilities)));
|
||||
allLabels.push(sample.label);
|
||||
} catch (error) {
|
||||
console.error("Processing error:", error);
|
||||
}
|
||||
}
|
||||
const elapsed = new Date().getTime() - t;
|
||||
|
||||
return calculateMetrics(allLabels, allPredictions, elapsed);
|
||||
}
|
||||
|
||||
// 主函数
|
||||
async function main() {
|
||||
await loadTokenizer();
|
||||
|
||||
// 评估原始模型
|
||||
const originalMetrics = await evaluateModel(sessionEmbeddingOriginal);
|
||||
console.log("Original Model Metrics:");
|
||||
console.table(originalMetrics);
|
||||
|
||||
// 评估量化模型
|
||||
const quantizedMetrics = await evaluateModel(sessionEmbeddingQuantized);
|
||||
console.log("Quantized Model Metrics:");
|
||||
console.table(quantizedMetrics);
|
||||
}
|
||||
|
||||
await main();
|
||||
@ -16,8 +16,8 @@ export const dispatchMilestoneSnapshotsWorker = async (_job: Job) => {
|
||||
if (eta > 144) continue;
|
||||
const now = Date.now();
|
||||
const scheduledNextSnapshotDelay = eta * HOUR;
|
||||
const maxInterval = 1 * HOUR;
|
||||
const minInterval = 1 * SECOND;
|
||||
const maxInterval = 1.2 * HOUR;
|
||||
const minInterval = 2 * SECOND;
|
||||
const delay = truncate(scheduledNextSnapshotDelay, minInterval, maxInterval);
|
||||
const targetTime = now + delay;
|
||||
await scheduleSnapshot(sql, aid, "milestone", targetTime);
|
||||
@ -25,5 +25,5 @@ export const dispatchMilestoneSnapshotsWorker = async (_job: Job) => {
|
||||
}
|
||||
} catch (e) {
|
||||
logger.error(e as Error, "mq", "fn:dispatchMilestoneSnapshotsWorker");
|
||||
};
|
||||
}
|
||||
}
|
||||
@ -1,7 +1,10 @@
|
||||
import { Job } from "bullmq";
|
||||
import { getLatestVideoSnapshot } from "db/snapshot.ts";
|
||||
import { truncate } from "utils/truncate.ts";
|
||||
import { getVideosWithoutActiveSnapshotSchedule, scheduleSnapshot } from "db/snapshotSchedule.ts";
|
||||
import {
|
||||
getVideosWithoutActiveSnapshotScheduleByType,
|
||||
scheduleSnapshot
|
||||
} from "db/snapshotSchedule.ts";
|
||||
import logger from "@core/log/logger.ts";
|
||||
import { HOUR, MINUTE, WEEK } from "@core/const/time.ts";
|
||||
import { lockManager } from "@core/mq/lockManager.ts";
|
||||
@ -17,7 +20,7 @@ export const dispatchRegularSnapshotsWorker = async (_job: Job): Promise<void> =
|
||||
}
|
||||
await lockManager.acquireLock("dispatchRegularSnapshots", 30 * 60);
|
||||
|
||||
const aids = await getVideosWithoutActiveSnapshotSchedule(sql);
|
||||
const aids = await getVideosWithoutActiveSnapshotScheduleByType(sql, "normal");
|
||||
for (const rawAid of aids) {
|
||||
const aid = Number(rawAid);
|
||||
const latestSnapshot = await getLatestVideoSnapshot(sql, aid);
|
||||
|
||||
@ -2,7 +2,6 @@ import { Job } from "bullmq";
|
||||
import { scheduleSnapshot, setSnapshotStatus, snapshotScheduleExists } from "db/snapshotSchedule.ts";
|
||||
import logger from "@core/log/logger.ts";
|
||||
import { HOUR, MINUTE, SECOND } from "@core/const/time.ts";
|
||||
import { lockManager } from "@core/mq/lockManager.ts";
|
||||
import { getBiliVideoStatus, setBiliVideoStatus } from "../../db/bilibili_metadata.ts";
|
||||
import { insertVideoSnapshot } from "mq/task/getVideoStats.ts";
|
||||
import { getSongsPublihsedAt } from "db/songs.ts";
|
||||
@ -78,8 +77,9 @@ export const snapshotVideoWorker = async (job: Job): Promise<void> => {
|
||||
logger.warn(
|
||||
`ETA (${etaHoursString}) too long for milestone snapshot. aid: ${aid}.`,
|
||||
"mq",
|
||||
"fn:dispatchRegularSnapshotsWorker",
|
||||
"fn:snapshotVideoWorker",
|
||||
);
|
||||
return;
|
||||
}
|
||||
const now = Date.now();
|
||||
const targetTime = now + eta * HOUR;
|
||||
@ -92,7 +92,7 @@ export const snapshotVideoWorker = async (job: Job): Promise<void> => {
|
||||
logger.warn(
|
||||
`No available proxy for aid ${job.data.aid}.`,
|
||||
"mq",
|
||||
"fn:takeSnapshotForVideoWorker",
|
||||
"fn:snapshotVideoWorker",
|
||||
);
|
||||
await setSnapshotStatus(sql, id, "no_proxy");
|
||||
await scheduleSnapshot(sql, aid, type, Date.now() + retryInterval);
|
||||
@ -102,16 +102,12 @@ export const snapshotVideoWorker = async (job: Job): Promise<void> => {
|
||||
logger.warn(
|
||||
`Failed to proxy request for aid ${job.data.aid}: ${e.message}`,
|
||||
"mq",
|
||||
"fn:takeSnapshotForVideoWorker",
|
||||
"fn:snapshotVideoWorker",
|
||||
);
|
||||
await setSnapshotStatus(sql, id, "failed");
|
||||
await scheduleSnapshot(sql, aid, type, Date.now() + retryInterval);
|
||||
}
|
||||
logger.error(e as Error, "mq", "fn:takeSnapshotForVideoWorker");
|
||||
logger.error(e as Error, "mq", "fn:snapshotVideoWorker");
|
||||
await setSnapshotStatus(sql, id, "failed");
|
||||
}
|
||||
finally {
|
||||
await lockManager.releaseLock("dispatchRegularSnapshots");
|
||||
};
|
||||
return;
|
||||
};
|
||||
|
||||
@ -2,7 +2,7 @@ import { findClosestSnapshot, getLatestSnapshot, hasAtLeast2Snapshots } from "db
|
||||
import { truncate } from "utils/truncate.ts";
|
||||
import { closetMilestone } from "./exec/snapshotTick.ts";
|
||||
import { HOUR, MINUTE } from "@core/const/time.ts";
|
||||
import type { Psql } from "@core/db/global.d.ts";
|
||||
import type { Psql } from "@core/db/psql.d.ts";
|
||||
|
||||
const log = (value: number, base: number = 10) => Math.log(value) / Math.log(base);
|
||||
|
||||
@ -34,7 +34,7 @@ export const getAdjustedShortTermETA = async (sql: Psql, aid: number) => {
|
||||
if (!snapshotsEnough) return 0;
|
||||
|
||||
const currentTimestamp = new Date().getTime();
|
||||
const timeIntervals = [3 * MINUTE, 20 * MINUTE, 1 * HOUR, 3 * HOUR, 6 * HOUR, 72 * HOUR];
|
||||
const timeIntervals = [3 * MINUTE, 20 * MINUTE, HOUR, 3 * HOUR, 6 * HOUR, 72 * HOUR];
|
||||
const DELTA = 0.00001;
|
||||
let minETAHours = Infinity;
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import { findClosestSnapshot, findSnapshotBefore, getLatestSnapshot } from "db/snapshotSchedule.ts";
|
||||
import { HOUR } from "@core/const/time.ts";
|
||||
import type { Psql } from "@core/db/global.d.ts";
|
||||
import type { Psql } from "@core/db/psql.d.ts";
|
||||
|
||||
export const getRegularSnapshotInterval = async (sql: Psql, aid: number) => {
|
||||
const now = Date.now();
|
||||
|
||||
Loading…
Reference in New Issue
Block a user