fix: incorrect import for psql.d.ts, remove unnecessary benchmark files
This commit is contained in:
parent
79a37d927a
commit
cf7a285f57
GPG Key ID:
56209E0CCD8420C6
@ -1,179 +0,0 @@
|
||||
import { AutoTokenizer, PreTrainedTokenizer } from "@huggingface/transformers";
|
||||
import * as ort from "onnxruntime";
|
||||
|
||||
function softmax(logits: Float32Array): number[] {
|
||||
const maxLogit = Math.max(...logits);
|
||||
const exponents = logits.map((logit) => Math.exp(logit - maxLogit));
|
||||
const sumOfExponents = exponents.reduce((sum, exp) => sum + exp, 0);
|
||||
return Array.from(exponents.map((exp) => exp / sumOfExponents));
|
||||
}
|
||||
|
||||
// 配置参数
|
||||
const sentenceTransformerModelName = "alikia2x/jina-embedding-v3-m2v-1024";
|
||||
const onnxClassifierPath = "./model/video_classifier_v3_17.onnx";
|
||||
const onnxEmbeddingPath = "./model/embedding_original.onnx";
|
||||
const testDataPath = "./data/filter/test1.jsonl";
|
||||
|
||||
// 初始化会话
|
||||
const [sessionClassifier, sessionEmbedding] = await Promise.all([
|
||||
ort.InferenceSession.create(onnxClassifierPath),
|
||||
ort.InferenceSession.create(onnxEmbeddingPath),
|
||||
]);
|
||||
|
||||
let tokenizer: PreTrainedTokenizer;
|
||||
|
||||
// 初始化分词器
|
||||
async function loadTokenizer() {
|
||||
const tokenizerConfig = { local_files_only: true };
|
||||
tokenizer = await AutoTokenizer.from_pretrained(sentenceTransformerModelName, tokenizerConfig);
|
||||
}
|
||||
|
||||
// 新的嵌入生成函数（使用ONNX）
|
||||
async function getONNXEmbeddings(texts: string[], session: ort.InferenceSession): Promise<number[]> {
|
||||
const { input_ids } = await tokenizer(texts, {
|
||||
add_special_tokens: false,
|
||||
return_tensor: false,
|
||||
});
|
||||
|
||||
// 构造输入参数
|
||||
const cumsum = (arr: number[]): number[] =>
|
||||
arr.reduce((acc: number[], num: number, i: number) => [...acc, num + (acc[i - 1] || 0)], []);
|
||||
|
||||
const offsets: number[] = [0, ...cumsum(input_ids.slice(0, -1).map((x: string) => x.length))];
|
||||
const flattened_input_ids = input_ids.flat();
|
||||
|
||||
// 准备ONNX输入
|
||||
const inputs = {
|
||||
input_ids: new ort.Tensor("int64", new BigInt64Array(flattened_input_ids.map(BigInt)), [
|
||||
flattened_input_ids.length,
|
||||
]),
|
||||
offsets: new ort.Tensor("int64", new BigInt64Array(offsets.map(BigInt)), [offsets.length]),
|
||||
};
|
||||
|
||||
// 执行推理
|
||||
const { embeddings } = await session.run(inputs);
|
||||
return Array.from(embeddings.data as Float32Array);
|
||||
}
|
||||
|
||||
// 分类推理函数
|
||||
async function runClassification(embeddings: number[]): Promise<number[]> {
|
||||
const inputTensor = new ort.Tensor(
|
||||
Float32Array.from(embeddings),
|
||||
[1, 3, 1024],
|
||||
);
|
||||
|
||||
const { logits } = await sessionClassifier.run({ channel_features: inputTensor });
|
||||
return softmax(logits.data as Float32Array);
|
||||
}
|
||||
|
||||
// 指标计算函数
|
||||
function calculateMetrics(labels: number[], predictions: number[], elapsedTime: number): {
|
||||
accuracy: number;
|
||||
precision: number;
|
||||
recall: number;
|
||||
f1: number;
|
||||
"Class 0 Prec": number;
|
||||
speed: string;
|
||||
} {
|
||||
// 输出label和prediction不一样的index列表
|
||||
const arr = [];
|
||||
for (let i = 0; i < labels.length; i++) {
|
||||
if (labels[i] !== predictions[i] && predictions[i] == 0) {
|
||||
arr.push([i + 1, labels[i], predictions[i]]);
|
||||
}
|
||||
}
|
||||
console.log(arr);
|
||||
// 初始化混淆矩阵
|
||||
const classCount = Math.max(...labels, ...predictions) + 1;
|
||||
const matrix = Array.from({ length: classCount }, () => Array.from({ length: classCount }, () => 0));
|
||||
|
||||
// 填充矩阵
|
||||
labels.forEach((trueLabel, i) => {
|
||||
matrix[trueLabel][predictions[i]]++;
|
||||
});
|
||||
|
||||
// 计算各指标
|
||||
let totalTP = 0, totalFP = 0, totalFN = 0;
|
||||
|
||||
for (let c = 0; c < classCount; c++) {
|
||||
const TP = matrix[c][c];
|
||||
const FP = matrix.flatMap((row, i) => i === c ? [] : [row[c]]).reduce((a, b) => a + b, 0);
|
||||
const FN = matrix[c].filter((_, i) => i !== c).reduce((a, b) => a + b, 0);
|
||||
|
||||
totalTP += TP;
|
||||
totalFP += FP;
|
||||
totalFN += FN;
|
||||
}
|
||||
|
||||
const precision = totalTP / (totalTP + totalFP);
|
||||
const recall = totalTP / (totalTP + totalFN);
|
||||
const f1 = 2 * (precision * recall) / (precision + recall) || 0;
|
||||
|
||||
// 计算Class 0 Precision
|
||||
const class0TP = matrix[0][0];
|
||||
const class0FP = matrix.flatMap((row, i) => i === 0 ? [] : [row[0]]).reduce((a, b) => a + b, 0);
|
||||
const class0Precision = class0TP / (class0TP + class0FP) || 0;
|
||||
|
||||
return {
|
||||
accuracy: labels.filter((l, i) => l === predictions[i]).length / labels.length,
|
||||
precision,
|
||||
recall,
|
||||
f1,
|
||||
speed: `${(labels.length / (elapsedTime / 1000)).toFixed(1)} samples/sec`,
|
||||
"Class 0 Prec": class0Precision,
|
||||
};
|
||||
}
|
||||
|
||||
// 改造后的评估函数
|
||||
async function evaluateModel(session: ort.InferenceSession): Promise<{
|
||||
accuracy: number;
|
||||
precision: number;
|
||||
recall: number;
|
||||
f1: number;
|
||||
"Class 0 Prec": number;
|
||||
}> {
|
||||
const data = await Deno.readTextFile(testDataPath);
|
||||
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();
|
||||
for (const sample of samples) {
|
||||
try {
|
||||
const embeddings = await getONNXEmbeddings([
|
||||
sample.title,
|
||||
sample.description,
|
||||
sample.tags.join(","),
|
||||
], session);
|
||||
|
||||
const probabilities = await runClassification(embeddings);
|
||||
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();
|
||||
|
||||
const metrics = await evaluateModel(sessionEmbedding);
|
||||
console.log("Model Metrics:");
|
||||
console.table(metrics);
|
||||
}
|
||||
|
||||
await main();
|
||||
@ -1,171 +0,0 @@
|
||||
import { AutoTokenizer, PreTrainedTokenizer } from "@huggingface/transformers";
|
||||
import * as ort from "onnxruntime";
|
||||
|
||||
function softmax(logits: Float32Array): number[] {
|
||||
const maxLogit = Math.max(...logits);
|
||||
const exponents = logits.map((logit) => Math.exp(logit - maxLogit));
|
||||
const sumOfExponents = exponents.reduce((sum, exp) => sum + exp, 0);
|
||||
return Array.from(exponents.map((exp) => exp / sumOfExponents));
|
||||
}
|
||||
|
||||
// 配置参数
|
||||
const sentenceTransformerModelName = "alikia2x/jina-embedding-v3-m2v-1024";
|
||||
const onnxClassifierPath = "./model/video_classifier_v3_11.onnx";
|
||||
const onnxEmbeddingOriginalPath = "./model/embedding_original.onnx";
|
||||
const onnxEmbeddingQuantizedPath = "./model/embedding_original.onnx";
|
||||
|
||||
// 初始化会话
|
||||
const [sessionClassifier, sessionEmbeddingOriginal, sessionEmbeddingQuantized] = await Promise.all([
|
||||
ort.InferenceSession.create(onnxClassifierPath),
|
||||
ort.InferenceSession.create(onnxEmbeddingOriginalPath),
|
||||
ort.InferenceSession.create(onnxEmbeddingQuantizedPath),
|
||||
]);
|
||||
|
||||
let tokenizer: PreTrainedTokenizer;
|
||||
|
||||
// 初始化分词器
|
||||
async function loadTokenizer() {
|
||||
const tokenizerConfig = { local_files_only: true };
|
||||
tokenizer = await AutoTokenizer.from_pretrained(sentenceTransformerModelName, tokenizerConfig);
|
||||
}
|
||||
|
||||
// 新的嵌入生成函数（使用ONNX）
|
||||
async function getONNXEmbeddings(texts: string[], session: ort.InferenceSession): Promise<number[]> {
|
||||
const { input_ids } = await tokenizer(texts, {
|
||||
add_special_tokens: false,
|
||||
return_tensor: false,
|
||||
});
|
||||
|
||||
// 构造输入参数
|
||||
const cumsum = (arr: number[]): number[] =>
|
||||
arr.reduce((acc: number[], num: number, i: number) => [...acc, num + (acc[i - 1] || 0)], []);
|
||||
|
||||
const offsets: number[] = [0, ...cumsum(input_ids.slice(0, -1).map((x: string) => x.length))];
|
||||
const flattened_input_ids = input_ids.flat();
|
||||
|
||||
// 准备ONNX输入
|
||||
const inputs = {
|
||||
input_ids: new ort.Tensor("int64", new BigInt64Array(flattened_input_ids.map(BigInt)), [
|
||||
flattened_input_ids.length,
|
||||
]),
|
||||
offsets: new ort.Tensor("int64", new BigInt64Array(offsets.map(BigInt)), [offsets.length]),
|
||||
};
|
||||
|
||||
// 执行推理
|
||||
const { embeddings } = await session.run(inputs);
|
||||
return Array.from(embeddings.data as Float32Array);
|
||||
}
|
||||
|
||||
// 分类推理函数
|
||||
async function runClassification(embeddings: number[]): Promise<number[]> {
|
||||
const inputTensor = new ort.Tensor(
|
||||
Float32Array.from(embeddings),
|
||||
[1, 4, 1024],
|
||||
);
|
||||
|
||||
const { logits } = await sessionClassifier.run({ channel_features: inputTensor });
|
||||
return softmax(logits.data as Float32Array);
|
||||
}
|
||||
|
||||
// 指标计算函数
|
||||
function calculateMetrics(labels: number[], predictions: number[], elapsedTime: number): {
|
||||
accuracy: number;
|
||||
precision: number;
|
||||
recall: number;
|
||||
f1: number;
|
||||
speed: string;
|
||||
} {
|
||||
// 初始化混淆矩阵
|
||||
const classCount = Math.max(...labels, ...predictions) + 1;
|
||||
const matrix = Array.from({ length: classCount }, () => Array.from({ length: classCount }, () => 0));
|
||||
|
||||
// 填充矩阵
|
||||
labels.forEach((trueLabel, i) => {
|
||||
matrix[trueLabel][predictions[i]]++;
|
||||
});
|
||||
|
||||
// 计算各指标
|
||||
let totalTP = 0, totalFP = 0, totalFN = 0;
|
||||
|
||||
for (let c = 0; c < classCount; c++) {
|
||||
const TP = matrix[c][c];
|
||||
const FP = matrix.flatMap((row, i) => i === c ? [] : [row[c]]).reduce((a, b) => a + b, 0);
|
||||
const FN = matrix[c].filter((_, i) => i !== c).reduce((a, b) => a + b, 0);
|
||||
|
||||
totalTP += TP;
|
||||
totalFP += FP;
|
||||
totalFN += FN;
|
||||
}
|
||||
|
||||
const precision = totalTP / (totalTP + totalFP);
|
||||
const recall = totalTP / (totalTP + totalFN);
|
||||
const f1 = 2 * (precision * recall) / (precision + recall) || 0;
|
||||
|
||||
return {
|
||||
accuracy: labels.filter((l, i) => l === predictions[i]).length / labels.length,
|
||||
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");
|
||||
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();
|
||||
for (const sample of samples) {
|
||||
try {
|
||||
const embeddings = await getONNXEmbeddings([
|
||||
sample.title,
|
||||
sample.description,
|
||||
sample.tags.join(","),
|
||||
sample.author_info,
|
||||
], session);
|
||||
|
||||
const probabilities = await runClassification(embeddings);
|
||||
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();
|
||||
@ -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);
|
||||
|
||||
|
||||
@ -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