mirror of
https://github.com/immich-app/immich.git
synced 2024-12-29 15:11:58 +00:00
feat(ml)!: customizable ML settings (#3891)
* consolidated endpoints, added live configuration * added ml settings to server * added settings dashboard * updated deps, fixed typos * simplified modelconfig updated tests * Added ml setting accordion for admin page updated tests * merge `clipText` and `clipVision` * added face distance setting clarified setting * add clip mode in request, dropdown for face models * polished ml settings updated descriptions * update clip field on error * removed unused import * add description for image classification threshold * pin safetensors for arm wheel updated poetry lock * moved dto * set model type only in ml repository * revert form-data package install use fetch instead of axios * added slotted description with link updated facial recognition description clarified effect of disabling tasks * validation before model load * removed unnecessary getconfig call * added migration * updated api updated api updated api --------- Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
This commit is contained in:
Mert
GitHub
parent
22f5e05060
commit
bcc36d14a1
56 changed files with 1424 additions and 620 deletions
154
cli/src/api/open-api/api.ts
generated
154
cli/src/api/open-api/api.ts
generated
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@ -862,6 +862,53 @@ export interface BulkIdsDto {
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*/
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'ids': Array<string>;
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}
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/**
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*
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* @export
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* @interface CLIPConfig
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*/
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export interface CLIPConfig {
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/**
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*
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* @type {boolean}
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* @memberof CLIPConfig
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*/
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'enabled': boolean;
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/**
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*
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* @type {CLIPMode}
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* @memberof CLIPConfig
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*/
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'mode'?: CLIPMode;
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/**
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*
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* @type {string}
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* @memberof CLIPConfig
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*/
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'modelName': string;
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/**
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*
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* @type {ModelType}
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* @memberof CLIPConfig
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*/
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'modelType'?: ModelType;
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}
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/**
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*
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* @export
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* @enum {string}
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*/
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export const CLIPMode = {
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Vision: 'vision',
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Text: 'text'
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} as const;
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export type CLIPMode = typeof CLIPMode[keyof typeof CLIPMode];
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/**
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*
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* @export
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@ -951,6 +998,39 @@ export interface CheckExistingAssetsResponseDto {
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*/
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'existingIds': Array<string>;
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}
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/**
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*
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* @export
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* @interface ClassificationConfig
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*/
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export interface ClassificationConfig {
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/**
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*
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* @type {boolean}
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* @memberof ClassificationConfig
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*/
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'enabled': boolean;
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/**
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*
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* @type {number}
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* @memberof ClassificationConfig
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*/
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'minScore': number;
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/**
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*
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* @type {string}
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* @memberof ClassificationConfig
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*/
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'modelName': string;
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/**
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*
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* @type {ModelType}
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* @memberof ClassificationConfig
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*/
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'modelType'?: ModelType;
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}
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/**
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*
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* @export
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@ -1766,6 +1846,21 @@ export interface MergePersonDto {
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*/
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'ids': Array<string>;
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}
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/**
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*
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* @export
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* @enum {string}
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*/
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export const ModelType = {
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ImageClassification: 'image-classification',
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FacialRecognition: 'facial-recognition',
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Clip: 'clip'
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} as const;
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export type ModelType = typeof ModelType[keyof typeof ModelType];
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/**
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*
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* @export
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@ -1991,6 +2086,45 @@ export interface QueueStatusDto {
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*/
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'isPaused': boolean;
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}
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/**
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*
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* @export
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* @interface RecognitionConfig
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*/
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export interface RecognitionConfig {
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/**
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*
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* @type {boolean}
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* @memberof RecognitionConfig
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*/
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'enabled': boolean;
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/**
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*
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* @type {number}
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* @memberof RecognitionConfig
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*/
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'maxDistance': number;
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/**
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*
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* @type {number}
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* @memberof RecognitionConfig
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*/
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'minScore': number;
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/**
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*
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* @type {string}
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* @memberof RecognitionConfig
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*/
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'modelName': string;
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/**
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*
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* @type {ModelType}
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* @memberof RecognitionConfig
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*/
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'modelType'?: ModelType;
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}
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/**
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*
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* @export
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@ -2803,10 +2937,16 @@ export interface SystemConfigJobDto {
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export interface SystemConfigMachineLearningDto {
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/**
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*
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* @type {boolean}
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* @type {ClassificationConfig}
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* @memberof SystemConfigMachineLearningDto
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*/
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'clipEncodeEnabled': boolean;
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'classification': ClassificationConfig;
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/**
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*
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* @type {CLIPConfig}
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* @memberof SystemConfigMachineLearningDto
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*/
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'clip': CLIPConfig;
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/**
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*
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* @type {boolean}
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@ -2815,16 +2955,10 @@ export interface SystemConfigMachineLearningDto {
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'enabled': boolean;
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/**
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*
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* @type {boolean}
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* @type {RecognitionConfig}
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* @memberof SystemConfigMachineLearningDto
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*/
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'facialRecognitionEnabled': boolean;
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/**
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*
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* @type {boolean}
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* @memberof SystemConfigMachineLearningDto
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*/
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'tagImageEnabled': boolean;
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'facialRecognition': RecognitionConfig;
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/**
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*
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* @type {string}
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@ -8,17 +8,11 @@ from .schemas import ModelType
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class Settings(BaseSettings):
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cache_folder: str = "/cache"
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classification_model: str = "microsoft/resnet-50"
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clip_image_model: str = "ViT-B-32::openai"
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clip_text_model: str = "ViT-B-32::openai"
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facial_recognition_model: str = "buffalo_l"
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min_tag_score: float = 0.9
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eager_startup: bool = False
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model_ttl: int = 0
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host: str = "0.0.0.0"
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port: int = 3003
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workers: int = 1
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min_face_score: float = 0.7
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test_full: bool = False
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request_threads: int = os.cpu_count() or 4
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model_inter_op_threads: int = 1
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@ -1,29 +1,26 @@
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import asyncio
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import os
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from concurrent.futures import ThreadPoolExecutor
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from io import BytesIO
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from typing import Any
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import cv2
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import numpy as np
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import orjson
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import uvicorn
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from fastapi import Body, Depends, FastAPI
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from PIL import Image
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from fastapi import FastAPI, Form, HTTPException, UploadFile
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from fastapi.responses import ORJSONResponse
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from starlette.formparsers import MultiPartParser
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from app.models.base import InferenceModel
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from .config import settings
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from .models.cache import ModelCache
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from .schemas import (
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EmbeddingResponse,
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FaceResponse,
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MessageResponse,
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ModelType,
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TagResponse,
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TextModelRequest,
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TextResponse,
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)
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MultiPartParser.max_file_size = 2**24 # spools to disk if payload is 16 MiB or larger
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app = FastAPI()
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@ -33,37 +30,9 @@ def init_state() -> None:
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app.state.thread_pool = ThreadPoolExecutor(settings.request_threads)
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async def load_models() -> None:
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models: list[tuple[str, ModelType, dict[str, Any]]] = [
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(settings.classification_model, ModelType.IMAGE_CLASSIFICATION, {}),
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(settings.clip_image_model, ModelType.CLIP, {"mode": "vision"}),
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(settings.clip_text_model, ModelType.CLIP, {"mode": "text"}),
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(settings.facial_recognition_model, ModelType.FACIAL_RECOGNITION, {}),
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]
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# Get all models
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for model_name, model_type, model_kwargs in models:
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await app.state.model_cache.get(model_name, model_type, eager=settings.eager_startup, **model_kwargs)
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@app.on_event("startup")
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async def startup_event() -> None:
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init_state()
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await load_models()
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@app.on_event("shutdown")
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async def shutdown_event() -> None:
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app.state.thread_pool.shutdown()
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def dep_pil_image(byte_image: bytes = Body(...)) -> Image.Image:
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return Image.open(BytesIO(byte_image))
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def dep_cv_image(byte_image: bytes = Body(...)) -> np.ndarray[int, np.dtype[Any]]:
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byte_image_np = np.frombuffer(byte_image, np.uint8)
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return cv2.imdecode(byte_image_np, cv2.IMREAD_COLOR)
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@app.get("/", response_model=MessageResponse)
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@ -76,57 +45,27 @@ def ping() -> str:
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return "pong"
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@app.post(
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"/image-classifier/tag-image",
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response_model=TagResponse,
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status_code=200,
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)
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async def image_classification(
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image: Image.Image = Depends(dep_pil_image),
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) -> list[str]:
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model = await app.state.model_cache.get(settings.classification_model, ModelType.IMAGE_CLASSIFICATION)
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labels = await predict(model, image)
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return labels
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@app.post("/predict")
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async def predict(
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model_name: str = Form(alias="modelName"),
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model_type: ModelType = Form(alias="modelType"),
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options: str = Form(default="{}"),
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text: str | None = Form(default=None),
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image: UploadFile | None = None,
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) -> Any:
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if image is not None:
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inputs: str | bytes = await image.read()
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elif text is not None:
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inputs = text
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else:
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raise HTTPException(400, "Either image or text must be provided")
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model: InferenceModel = await app.state.model_cache.get(model_name, model_type, **orjson.loads(options))
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outputs = await run(model, inputs)
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return ORJSONResponse(outputs)
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@app.post(
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"/sentence-transformer/encode-image",
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response_model=EmbeddingResponse,
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status_code=200,
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)
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async def clip_encode_image(
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image: Image.Image = Depends(dep_pil_image),
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) -> list[float]:
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model = await app.state.model_cache.get(settings.clip_image_model, ModelType.CLIP, mode="vision")
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embedding = await predict(model, image)
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return embedding
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@app.post(
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"/sentence-transformer/encode-text",
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response_model=EmbeddingResponse,
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status_code=200,
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)
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async def clip_encode_text(payload: TextModelRequest) -> list[float]:
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model = await app.state.model_cache.get(settings.clip_text_model, ModelType.CLIP, mode="text")
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embedding = await predict(model, payload.text)
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return embedding
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@app.post(
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"/facial-recognition/detect-faces",
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response_model=FaceResponse,
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status_code=200,
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)
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async def facial_recognition(
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image: cv2.Mat = Depends(dep_cv_image),
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) -> list[dict[str, Any]]:
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model = await app.state.model_cache.get(settings.facial_recognition_model, ModelType.FACIAL_RECOGNITION)
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faces = await predict(model, image)
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return faces
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async def predict(model: InferenceModel, inputs: Any) -> Any:
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async def run(model: InferenceModel, inputs: Any) -> Any:
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return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict, inputs)
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@ -60,16 +60,21 @@ class InferenceModel(ABC):
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self._load(**model_kwargs)
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self._loaded = True
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def predict(self, inputs: Any) -> Any:
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def predict(self, inputs: Any, **model_kwargs: Any) -> Any:
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if not self._loaded:
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print(f"Loading {self.model_type.value.replace('_', ' ')} model...")
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self.load()
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if model_kwargs:
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self.configure(**model_kwargs)
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return self._predict(inputs)
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@abstractmethod
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def _predict(self, inputs: Any) -> Any:
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...
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def configure(self, **model_kwargs: Any) -> None:
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pass
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@abstractmethod
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def _download(self, **model_kwargs: Any) -> None:
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...
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@ -1,5 +1,6 @@
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import os
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import zipfile
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from io import BytesIO
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from typing import Any, Literal
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import onnxruntime as ort
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@ -8,7 +9,7 @@ from clip_server.model.clip import BICUBIC, _convert_image_to_rgb
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from clip_server.model.clip_onnx import _MODELS, _S3_BUCKET_V2, CLIPOnnxModel, download_model
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from clip_server.model.pretrained_models import _VISUAL_MODEL_IMAGE_SIZE
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from clip_server.model.tokenization import Tokenizer
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from PIL.Image import Image
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from PIL import Image
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from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
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from ..schemas import ModelType
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@ -74,9 +75,12 @@ class CLIPEncoder(InferenceModel):
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image_size = _VISUAL_MODEL_IMAGE_SIZE[CLIPOnnxModel.get_model_name(self.model_name)]
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self.transform = _transform_pil_image(image_size)
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def _predict(self, image_or_text: Image | str) -> list[float]:
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def _predict(self, image_or_text: Image.Image | str) -> list[float]:
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if isinstance(image_or_text, bytes):
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image_or_text = Image.open(BytesIO(image_or_text))
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match image_or_text:
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case Image():
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case Image.Image():
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if self.mode == "text":
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raise TypeError("Cannot encode image as text-only model")
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pixel_values = self.transform(image_or_text)
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|
|
|
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|
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@ -9,7 +9,6 @@ from insightface.model_zoo import ArcFaceONNX, RetinaFace
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from insightface.utils.face_align import norm_crop
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from insightface.utils.storage import BASE_REPO_URL, download_file
|
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|
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from ..config import settings
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from ..schemas import ModelType
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from .base import InferenceModel
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|
@ -20,7 +19,7 @@ class FaceRecognizer(InferenceModel):
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def __init__(
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self,
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model_name: str,
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min_score: float = settings.min_face_score,
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min_score: float = 0.7,
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cache_dir: Path | str | None = None,
|
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**model_kwargs: Any,
|
||||
) -> None:
|
||||
|
|
@ -69,11 +68,13 @@ class FaceRecognizer(InferenceModel):
|
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)
|
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self.rec_model.prepare(ctx_id=0)
|
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|
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def _predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
|
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def _predict(self, image: np.ndarray[int, np.dtype[Any]] | bytes) -> list[dict[str, Any]]:
|
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if isinstance(image, bytes):
|
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image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
|
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bboxes, kpss = self.det_model.detect(image)
|
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if bboxes.size == 0:
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return []
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assert isinstance(kpss, np.ndarray)
|
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assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray)
|
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|
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scores = bboxes[:, 4].tolist()
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bboxes = bboxes[:, :4].round().tolist()
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|
|
@ -102,3 +103,6 @@ class FaceRecognizer(InferenceModel):
|
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@property
|
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def cached(self) -> bool:
|
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return self.cache_dir.is_dir() and any(self.cache_dir.glob("*.onnx"))
|
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|
||||
def configure(self, **model_kwargs: Any) -> None:
|
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self.det_model.det_thresh = model_kwargs.get("min_score", self.det_model.det_thresh)
|
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|
|
|
|||
|
|
@ -1,13 +1,13 @@
|
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from io import BytesIO
|
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from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from huggingface_hub import snapshot_download
|
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from optimum.onnxruntime import ORTModelForImageClassification
|
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from optimum.pipelines import pipeline
|
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from PIL.Image import Image
|
||||
from PIL import Image
|
||||
from transformers import AutoImageProcessor
|
||||
|
||||
from ..config import settings
|
||||
from ..schemas import ModelType
|
||||
from .base import InferenceModel
|
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|
||||
|
|
@ -18,7 +18,7 @@ class ImageClassifier(InferenceModel):
|
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def __init__(
|
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self,
|
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model_name: str,
|
||||
min_score: float = settings.min_tag_score,
|
||||
min_score: float = 0.9,
|
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cache_dir: Path | str | None = None,
|
||||
**model_kwargs: Any,
|
||||
) -> None:
|
||||
|
|
@ -56,8 +56,13 @@ class ImageClassifier(InferenceModel):
|
|||
feature_extractor=processor,
|
||||
)
|
||||
|
||||
def _predict(self, image: Image) -> list[str]:
|
||||
def _predict(self, image: Image.Image | bytes) -> list[str]:
|
||||
if isinstance(image, bytes):
|
||||
image = Image.open(BytesIO(image))
|
||||
predictions: list[dict[str, Any]] = self.model(image) # type: ignore
|
||||
tags = [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score]
|
||||
|
||||
return tags
|
||||
|
||||
def configure(self, **model_kwargs: Any) -> None:
|
||||
self.min_score = model_kwargs.get("min_score", self.min_score)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from enum import Enum
|
||||
from enum import StrEnum
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
|
@ -20,18 +20,6 @@ class MessageResponse(BaseModel):
|
|||
message: str
|
||||
|
||||
|
||||
class TagResponse(BaseModel):
|
||||
__root__: list[str]
|
||||
|
||||
|
||||
class Embedding(BaseModel):
|
||||
__root__: list[float]
|
||||
|
||||
|
||||
class EmbeddingResponse(BaseModel):
|
||||
__root__: Embedding
|
||||
|
||||
|
||||
class BoundingBox(BaseModel):
|
||||
x1: int
|
||||
y1: int
|
||||
|
|
@ -39,23 +27,7 @@ class BoundingBox(BaseModel):
|
|||
y2: int
|
||||
|
||||
|
||||
class Face(BaseModel):
|
||||
image_width: int
|
||||
image_height: int
|
||||
bounding_box: BoundingBox
|
||||
score: float
|
||||
embedding: Embedding
|
||||
|
||||
class Config:
|
||||
alias_generator = to_lower_camel
|
||||
allow_population_by_field_name = True
|
||||
|
||||
|
||||
class FaceResponse(BaseModel):
|
||||
__root__: list[Face]
|
||||
|
||||
|
||||
class ModelType(Enum):
|
||||
class ModelType(StrEnum):
|
||||
IMAGE_CLASSIFICATION = "image-classification"
|
||||
CLIP = "clip"
|
||||
FACIAL_RECOGNITION = "facial-recognition"
|
||||
|
|
|
|||
799
machine-learning/poetry.lock
generated
799
machine-learning/poetry.lock
generated
|
|
@ -720,69 +720,69 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "cython"
|
||||
version = "3.0.0"
|
||||
version = "3.0.2"
|
||||
description = "The Cython compiler for writing C extensions in the Python language."
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
files = [
|
||||
{file = "Cython-3.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7c7d728e1a49ad01d41181e3a9ea80b8d14e825f4679e4dd837cbf7bca7998a5"},
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||||
{file = "Cython-3.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:626a4a6ef4b7ced87c348ea805488e4bd39dad9d0b39659aa9e1040b62bbfedf"},
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||||
{file = "Cython-3.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33c900d1ca9f622b969ac7d8fc44bdae140a4a6c7d8819413b51f3ccd0586a09"},
|
||||
{file = "Cython-3.0.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a65bc50dc1bc2faeafd9425defbdef6a468974f5c4192497ff7f14adccfdcd32"},
|
||||
{file = "Cython-3.0.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:3b71b399b10b038b056ad12dce1e317a8aa7a96e99de7e4fa2fa5d1c9415cfb9"},
|
||||
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||||
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]
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||||
[[package]]
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||||
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|
@ -889,13 +889,13 @@ testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "p
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|||
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||||
[[package]]
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||||
name = "flask"
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||||
version = "2.3.2"
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||||
version = "2.3.3"
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||||
description = "A simple framework for building complex web applications."
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||||
optional = false
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python-versions = ">=3.8"
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[package.dependencies]
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|
|
@ -903,7 +903,7 @@ blinker = ">=1.6.2"
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|||
click = ">=8.1.3"
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itsdangerous = ">=2.1.2"
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Jinja2 = ">=3.1.2"
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Werkzeug = ">=2.3.3"
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Werkzeug = ">=2.3.7"
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[package.extras]
|
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async = ["asgiref (>=3.2)"]
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|
@ -1185,101 +1185,119 @@ test = ["cffi (>=1.12.2)", "coverage (>=5.0)", "dnspython (>=1.16.0,<2.0)", "idn
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[[package]]
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{file = "geventhttpclient-2.0.10.tar.gz", hash = "sha256:b7c97b26511957a36a894ec54651c08890a69e118b69755f8e74bfc37c63391b"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1614,13 +1632,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "imageio"
|
||||
version = "2.31.1"
|
||||
version = "2.31.2"
|
||||
description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "imageio-2.31.1-py3-none-any.whl", hash = "sha256:4106fb395ef7f8dc0262d6aa1bb03daba818445c381ca8b7d5dfc7a2089b04df"},
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||||
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1720,79 +1738,115 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "kiwisolver"
|
||||
version = "1.4.4"
|
||||
version = "1.4.5"
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||||
description = "A fast implementation of the Cassowary constraint solver"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
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files = [
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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2302,42 +2356,42 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "onnx"
|
||||
version = "1.14.0"
|
||||
version = "1.14.1"
|
||||
description = "Open Neural Network Exchange"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
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{file = "onnx-1.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:921ad325b17484698d9d65978e123b1f351328ea50de6f84f25d09d5c7dde361"},
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{file = "onnx-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b9cd91b85cfbb0d6478f4a1a0aee4d95cf8839adc48c69130a0cf8452f21db4"},
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{file = "onnx-1.14.1-cp37-cp37m-macosx_10_12_universal2.whl", hash = "sha256:3fde9e1854e525aae93b403c1174bf68dc86ac92b6f8fb4af0fe3ec0d1440631"},
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|
||||
{file = "onnx-1.14.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:498ecc3e545b80685501c26b62eeeda0b8ae2f2ba8ff3f650ce1f526924aa699"},
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||||
{file = "onnx-1.14.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e991e867b799df0d7ed4cdad94c6a3ed9bebaceef3e574ac9eed314e1bfca0ef"},
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||||
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||||
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||||
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|
||||
{file = "onnx-1.14.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:030aa47e28337fd81f4d884032660e40912a4763ce4e5a4b4144380271390e82"},
|
||||
{file = "onnx-1.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6b113fa0183034743e6477fec928e478a6d94eee8d9a4376c144d20d736cdc45"},
|
||||
{file = "onnx-1.14.1-cp39-cp39-win32.whl", hash = "sha256:b9c28a99d4a620cb1d31120d35e0fab54073b9725ed50c3cd3ec7beb876e8dba"},
|
||||
{file = "onnx-1.14.1-cp39-cp39-win_amd64.whl", hash = "sha256:bdb15fc4b7f2a8a19abb52ac9672db876f9505e7219e206bcb7530e7c1274e55"},
|
||||
{file = "onnx-1.14.1.tar.gz", hash = "sha256:70903afe163643bd71195c78cedcc3f4fa05a2af651fd950ef3acbb15175b2d1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2442,13 +2496,13 @@ numpy = [
|
|||
|
||||
[[package]]
|
||||
name = "optimum"
|
||||
version = "1.11.2"
|
||||
version = "1.12.0"
|
||||
description = "Optimum Library is an extension of the Hugging Face Transformers library, providing a framework to integrate third-party libraries from Hardware Partners and interface with their specific functionality."
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "optimum-1.11.2-py3-none-any.whl", hash = "sha256:1382d923f95e053db677b1bc84803828921b41dd15d094cc61deab33ab5d4fb2"},
|
||||
{file = "optimum-1.11.2.tar.gz", hash = "sha256:664fa01aa4734d13ba291d41e7b901d333cea45b6c55d66098f43160bf7533ab"},
|
||||
{file = "optimum-1.12.0-py3-none-any.whl", hash = "sha256:4eb2e800b5ef52aa4c744b7494aa2000be8b583dfc99dddd5c0e9384ea0d77a0"},
|
||||
{file = "optimum-1.12.0.tar.gz", hash = "sha256:a74e051c4d776a900b6452a12a36e0afadbebee112a8205a30adac9216a4991b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2468,7 +2522,7 @@ diffusers = ["diffusers"]
|
|||
doc-build = ["accelerate"]
|
||||
exporters = ["onnx", "onnxruntime", "timm"]
|
||||
exporters-gpu = ["onnx", "onnxruntime-gpu", "timm"]
|
||||
exporters-tf = ["h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4)", "tf2onnx", "timm"]
|
||||
exporters-tf = ["h5py", "numpy (<1.24.0)", "onnx", "onnxruntime", "tensorflow (>=2.4,<=2.12.1)", "tf2onnx", "timm"]
|
||||
furiosa = ["optimum-furiosa"]
|
||||
graphcore = ["optimum-graphcore"]
|
||||
habana = ["optimum-habana"]
|
||||
|
|
@ -2483,6 +2537,75 @@ openvino = ["optimum-intel[openvino] (>=1.10.1)"]
|
|||
quality = ["black (>=23.1,<24.0)", "ruff (>=0.0.241,<=0.0.259)"]
|
||||
tests = ["Pillow", "diffusers (>=0.17.0)", "einops", "invisible-watermark", "parameterized", "pytest", "pytest-xdist", "requests", "sacremoses", "torchaudio", "torchvision"]
|
||||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.9.5"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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||||
[[package]]
|
||||
name = "packaging"
|
||||
version = "23.1"
|
||||
|
|
@ -2667,13 +2790,13 @@ test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-co
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|||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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||||
optional = false
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||||
python-versions = ">=3.7"
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python-versions = ">=3.8"
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||||
[package.extras]
|
||||
|
|
@ -2755,36 +2878,40 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
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|||
|
||||
[[package]]
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||||
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|
||||
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]
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||||
[package.dependencies]
|
||||
|
|
@ -2993,6 +3120,20 @@ files = [
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|||
[package.extras]
|
||||
cli = ["click (>=5.0)"]
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||||
|
||||
[[package]]
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||||
name = "python-multipart"
|
||||
version = "0.0.6"
|
||||
description = "A streaming multipart parser for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
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files = [
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]
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[package.extras]
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|
||||
|
||||
[[package]]
|
||||
name = "pytz"
|
||||
version = "2023.3"
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|
|
@ -3736,13 +3877,13 @@ files = [
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|||
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||||
[[package]]
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||||
name = "tifffile"
|
||||
version = "2023.8.12"
|
||||
version = "2023.8.25"
|
||||
description = "Read and write TIFF files"
|
||||
optional = false
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python-versions = ">=3.9"
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files = [
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{file = "tifffile-2023.8.12-py3-none-any.whl", hash = "sha256:d1ef06461a947a6800ba6121b330b54a57fb9cbf7e5bc0adab8307081297d66b"},
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]
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[package.dependencies]
|
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|
|
@ -3985,18 +4126,18 @@ telegram = ["requests"]
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|||
|
||||
[[package]]
|
||||
name = "transformers"
|
||||
version = "4.31.0"
|
||||
version = "4.32.0"
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||||
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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optional = false
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python-versions = ">=3.8.0"
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files = [
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]
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||||
[package.dependencies]
|
||||
filelock = "*"
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||||
huggingface-hub = ">=0.14.1,<1.0"
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||||
huggingface-hub = ">=0.15.1,<1.0"
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||||
numpy = ">=1.17"
|
||||
packaging = ">=20.0"
|
||||
protobuf = {version = "*", optional = true, markers = "extra == \"sentencepiece\""}
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|
|
@ -4011,18 +4152,18 @@ tqdm = ">=4.27"
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|||
[package.extras]
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accelerate = ["accelerate (>=0.20.3)"]
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|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.2.8,!=0.3.2,<=0.4.13)", "jaxlib (>=0.1.65,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
|
||||
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
ftfy = ["ftfy"]
|
||||
integrations = ["optuna", "ray[tune]", "sigopt"]
|
||||
|
|
@ -4050,7 +4191,7 @@ tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
|
|||
torch = ["accelerate (>=0.20.3)", "torch (>=1.9,!=1.12.0)"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (<10.0.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.14.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
|
||||
video = ["av (==9.2.0)", "decord (==0.6.0)"]
|
||||
vision = ["Pillow (<10.0.0)"]
|
||||
|
||||
|
|
@ -4164,33 +4305,33 @@ test = ["Cython (>=0.29.32,<0.30.0)", "aiohttp", "flake8 (>=3.9.2,<3.10.0)", "my
|
|||
|
||||
[[package]]
|
||||
name = "watchfiles"
|
||||
version = "0.19.0"
|
||||
version = "0.20.0"
|
||||
description = "Simple, modern and high performance file watching and code reload in python."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-macosx_10_7_x86_64.whl", hash = "sha256:91633e64712df3051ca454ca7d1b976baf842d7a3640b87622b323c55f3345e7"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:b6577b8c6c8701ba8642ea9335a129836347894b666dd1ec2226830e263909d3"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:18b28f6ad871b82df9542ff958d0c86bb0d8310bb09eb8e87d97318a3b5273af"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fac19dc9cbc34052394dbe81e149411a62e71999c0a19e1e09ce537867f95ae0"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:09ea3397aecbc81c19ed7f025e051a7387feefdb789cf768ff994c1228182fda"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c0376deac92377817e4fb8f347bf559b7d44ff556d9bc6f6208dd3f79f104aaf"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c75eff897786ee262c9f17a48886f4e98e6cfd335e011c591c305e5d083c056"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb5d45c4143c1dd60f98a16187fd123eda7248f84ef22244818c18d531a249d1"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:79c533ff593db861ae23436541f481ec896ee3da4e5db8962429b441bbaae16e"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:3d7d267d27aceeeaa3de0dd161a0d64f0a282264d592e335fff7958cc0cbae7c"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-win32.whl", hash = "sha256:176a9a7641ec2c97b24455135d58012a5be5c6217fc4d5fef0b2b9f75dbf5154"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-win_amd64.whl", hash = "sha256:945be0baa3e2440151eb3718fd8846751e8b51d8de7b884c90b17d271d34cae8"},
|
||||
{file = "watchfiles-0.19.0-cp37-abi3-win_arm64.whl", hash = "sha256:0089c6dc24d436b373c3c57657bf4f9a453b13767150d17284fc6162b2791911"},
|
||||
{file = "watchfiles-0.19.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:cae3dde0b4b2078f31527acff6f486e23abed307ba4d3932466ba7cdd5ecec79"},
|
||||
{file = "watchfiles-0.19.0-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:7f3920b1285a7d3ce898e303d84791b7bf40d57b7695ad549dc04e6a44c9f120"},
|
||||
{file = "watchfiles-0.19.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9afd0d69429172c796164fd7fe8e821ade9be983f51c659a38da3faaaaac44dc"},
|
||||
{file = "watchfiles-0.19.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:68dce92b29575dda0f8d30c11742a8e2b9b8ec768ae414b54f7453f27bdf9545"},
|
||||
{file = "watchfiles-0.19.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:5569fc7f967429d4bc87e355cdfdcee6aabe4b620801e2cf5805ea245c06097c"},
|
||||
{file = "watchfiles-0.19.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:5471582658ea56fca122c0f0d0116a36807c63fefd6fdc92c71ca9a4491b6b48"},
|
||||
{file = "watchfiles-0.19.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b538014a87f94d92f98f34d3e6d2635478e6be6423a9ea53e4dd96210065e193"},
|
||||
{file = "watchfiles-0.19.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20b44221764955b1e703f012c74015306fb7e79a00c15370785f309b1ed9aa8d"},
|
||||
{file = "watchfiles-0.19.0.tar.gz", hash = "sha256:d9b073073e048081e502b6c6b0b88714c026a1a4c890569238d04aca5f9ca74b"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-macosx_10_7_x86_64.whl", hash = "sha256:3796312bd3587e14926013612b23066912cf45a14af71cf2b20db1c12dadf4e9"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:d0002d81c89a662b595645fb684a371b98ff90a9c7d8f8630c82f0fde8310458"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:570848706440373b4cd8017f3e850ae17f76dbdf1e9045fc79023b11e1afe490"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a0351d20d03c6f7ad6b2e8a226a5efafb924c7755ee1e34f04c77c3682417fa"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:007dcc4a401093010b389c044e81172c8a2520dba257c88f8828b3d460c6bb38"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0d82dbc1832da83e441d112069833eedd4cf583d983fb8dd666fbefbea9d99c0"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:99f4c65fd2fce61a571b2a6fcf747d6868db0bef8a934e8ca235cc8533944d95"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5392dd327a05f538c56edb1c6ebba6af91afc81b40822452342f6da54907bbdf"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:08dc702529bb06a2b23859110c214db245455532da5eaea602921687cfcd23db"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:7d4e66a857621584869cfbad87039e65dadd7119f0d9bb9dbc957e089e32c164"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-win32.whl", hash = "sha256:a03d1e6feb7966b417f43c3e3783188167fd69c2063e86bad31e62c4ea794cc5"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-win_amd64.whl", hash = "sha256:eccc8942bcdc7d638a01435d915b913255bbd66f018f1af051cd8afddb339ea3"},
|
||||
{file = "watchfiles-0.20.0-cp37-abi3-win_arm64.whl", hash = "sha256:b17d4176c49d207865630da5b59a91779468dd3e08692fe943064da260de2c7c"},
|
||||
{file = "watchfiles-0.20.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:d97db179f7566dcf145c5179ddb2ae2a4450e3a634eb864b09ea04e68c252e8e"},
|
||||
{file = "watchfiles-0.20.0-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:835df2da7a5df5464c4a23b2d963e1a9d35afa422c83bf4ff4380b3114603644"},
|
||||
{file = "watchfiles-0.20.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:608cd94a8767f49521901aff9ae0c92cc8f5a24d528db7d6b0295290f9d41193"},
|
||||
{file = "watchfiles-0.20.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:89d1de8218874925bce7bb2ae9657efc504411528930d7a83f98b1749864f2ef"},
|
||||
{file = "watchfiles-0.20.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:13f995d5152a8ba4ed7c2bbbaeee4e11a5944defc7cacd0ccb4dcbdcfd78029a"},
|
||||
{file = "watchfiles-0.20.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:9b5c8d3be7b502f8c43a33c63166ada8828dbb0c6d49c8f9ce990a96de2f5a49"},
|
||||
{file = "watchfiles-0.20.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e43af4464daa08723c04b43cf978ab86cc55c684c16172622bdac64b34e36af0"},
|
||||
{file = "watchfiles-0.20.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87d9e1f75c4f86c93d73b5bd1ebe667558357548f11b4f8af4e0e272f79413ce"},
|
||||
{file = "watchfiles-0.20.0.tar.gz", hash = "sha256:728575b6b94c90dd531514677201e8851708e6e4b5fe7028ac506a200b622019"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4552,4 +4693,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "1a8981686295b3de58b41192c608a69a2c05983c47f93451c8a6ed50304b7cb3"
|
||||
content-hash = "6d200d3ea1ccf9fb89f44043e3e0845e70f19aac374b96227559375f44508dc5"
|
||||
|
|
|
|||
|
|
@ -30,6 +30,9 @@ rich = "^13.4.2"
|
|||
ftfy = "^6.1.1"
|
||||
setuptools = "^68.0.0"
|
||||
open-clip-torch = "^2.20.0"
|
||||
python-multipart = "^0.0.6"
|
||||
orjson = "^3.9.5"
|
||||
safetensors = "0.3.2"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
mypy = "^1.3.0"
|
||||
|
|
|
|||
15
mobile/openapi/.openapi-generator/FILES
generated
15
mobile/openapi/.openapi-generator/FILES
generated
|
|
@ -35,11 +35,14 @@ doc/AuthDeviceResponseDto.md
|
|||
doc/AuthenticationApi.md
|
||||
doc/BulkIdResponseDto.md
|
||||
doc/BulkIdsDto.md
|
||||
doc/CLIPConfig.md
|
||||
doc/CLIPMode.md
|
||||
doc/ChangePasswordDto.md
|
||||
doc/CheckDuplicateAssetDto.md
|
||||
doc/CheckDuplicateAssetResponseDto.md
|
||||
doc/CheckExistingAssetsDto.md
|
||||
doc/CheckExistingAssetsResponseDto.md
|
||||
doc/ClassificationConfig.md
|
||||
doc/CreateAlbumDto.md
|
||||
doc/CreateProfileImageResponseDto.md
|
||||
doc/CreateTagDto.md
|
||||
|
|
@ -68,6 +71,7 @@ doc/LogoutResponseDto.md
|
|||
doc/MapMarkerResponseDto.md
|
||||
doc/MemoryLaneResponseDto.md
|
||||
doc/MergePersonDto.md
|
||||
doc/ModelType.md
|
||||
doc/OAuthApi.md
|
||||
doc/OAuthCallbackDto.md
|
||||
doc/OAuthConfigDto.md
|
||||
|
|
@ -80,6 +84,7 @@ doc/PersonApi.md
|
|||
doc/PersonResponseDto.md
|
||||
doc/PersonUpdateDto.md
|
||||
doc/QueueStatusDto.md
|
||||
doc/RecognitionConfig.md
|
||||
doc/SearchAlbumResponseDto.md
|
||||
doc/SearchApi.md
|
||||
doc/SearchAssetDto.md
|
||||
|
|
@ -189,6 +194,9 @@ lib/model/check_duplicate_asset_dto.dart
|
|||
lib/model/check_duplicate_asset_response_dto.dart
|
||||
lib/model/check_existing_assets_dto.dart
|
||||
lib/model/check_existing_assets_response_dto.dart
|
||||
lib/model/classification_config.dart
|
||||
lib/model/clip_config.dart
|
||||
lib/model/clip_mode.dart
|
||||
lib/model/create_album_dto.dart
|
||||
lib/model/create_profile_image_response_dto.dart
|
||||
lib/model/create_tag_dto.dart
|
||||
|
|
@ -216,6 +224,7 @@ lib/model/logout_response_dto.dart
|
|||
lib/model/map_marker_response_dto.dart
|
||||
lib/model/memory_lane_response_dto.dart
|
||||
lib/model/merge_person_dto.dart
|
||||
lib/model/model_type.dart
|
||||
lib/model/o_auth_callback_dto.dart
|
||||
lib/model/o_auth_config_dto.dart
|
||||
lib/model/o_auth_config_response_dto.dart
|
||||
|
|
@ -225,6 +234,7 @@ lib/model/people_update_item.dart
|
|||
lib/model/person_response_dto.dart
|
||||
lib/model/person_update_dto.dart
|
||||
lib/model/queue_status_dto.dart
|
||||
lib/model/recognition_config.dart
|
||||
lib/model/search_album_response_dto.dart
|
||||
lib/model/search_asset_dto.dart
|
||||
lib/model/search_asset_response_dto.dart
|
||||
|
|
@ -309,6 +319,9 @@ test/check_duplicate_asset_dto_test.dart
|
|||
test/check_duplicate_asset_response_dto_test.dart
|
||||
test/check_existing_assets_dto_test.dart
|
||||
test/check_existing_assets_response_dto_test.dart
|
||||
test/classification_config_test.dart
|
||||
test/clip_config_test.dart
|
||||
test/clip_mode_test.dart
|
||||
test/create_album_dto_test.dart
|
||||
test/create_profile_image_response_dto_test.dart
|
||||
test/create_tag_dto_test.dart
|
||||
|
|
@ -337,6 +350,7 @@ test/logout_response_dto_test.dart
|
|||
test/map_marker_response_dto_test.dart
|
||||
test/memory_lane_response_dto_test.dart
|
||||
test/merge_person_dto_test.dart
|
||||
test/model_type_test.dart
|
||||
test/o_auth_api_test.dart
|
||||
test/o_auth_callback_dto_test.dart
|
||||
test/o_auth_config_dto_test.dart
|
||||
|
|
@ -349,6 +363,7 @@ test/person_api_test.dart
|
|||
test/person_response_dto_test.dart
|
||||
test/person_update_dto_test.dart
|
||||
test/queue_status_dto_test.dart
|
||||
test/recognition_config_test.dart
|
||||
test/search_album_response_dto_test.dart
|
||||
test/search_api_test.dart
|
||||
test/search_asset_dto_test.dart
|
||||
|
|
|
|||
BIN
mobile/openapi/README.md
generated
BIN
mobile/openapi/README.md
generated
Binary file not shown.
BIN
mobile/openapi/doc/CLIPConfig.md
generated
Normal file
BIN
mobile/openapi/doc/CLIPConfig.md
generated
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mobile/openapi/doc/CLIPMode.md
generated
Normal file
BIN
mobile/openapi/doc/CLIPMode.md
generated
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mobile/openapi/doc/ClassificationConfig.md
generated
Normal file
BIN
mobile/openapi/doc/ClassificationConfig.md
generated
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mobile/openapi/doc/ModelType.md
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mobile/openapi/doc/ModelType.md
generated
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mobile/openapi/doc/RecognitionConfig.md
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mobile/openapi/doc/RecognitionConfig.md
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mobile/openapi/doc/SystemConfigMachineLearningDto.md
generated
BIN
mobile/openapi/doc/SystemConfigMachineLearningDto.md
generated
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generated
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generated
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mobile/openapi/lib/api_helper.dart
generated
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mobile/openapi/lib/model/classification_config.dart
generated
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mobile/openapi/lib/model/classification_config.dart
generated
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mobile/openapi/lib/model/clip_config.dart
generated
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mobile/openapi/lib/model/clip_config.dart
generated
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mobile/openapi/lib/model/clip_mode.dart
generated
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mobile/openapi/lib/model/clip_mode.dart
generated
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mobile/openapi/lib/model/model_type.dart
generated
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mobile/openapi/lib/model/model_type.dart
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Binary file not shown.
BIN
mobile/openapi/lib/model/recognition_config.dart
generated
Normal file
BIN
mobile/openapi/lib/model/recognition_config.dart
generated
Normal file
Binary file not shown.
Binary file not shown.
BIN
mobile/openapi/test/classification_config_test.dart
generated
Normal file
BIN
mobile/openapi/test/classification_config_test.dart
generated
Normal file
Binary file not shown.
BIN
mobile/openapi/test/clip_config_test.dart
generated
Normal file
BIN
mobile/openapi/test/clip_config_test.dart
generated
Normal file
Binary file not shown.
BIN
mobile/openapi/test/clip_mode_test.dart
generated
Normal file
BIN
mobile/openapi/test/clip_mode_test.dart
generated
Normal file
Binary file not shown.
BIN
mobile/openapi/test/model_type_test.dart
generated
Normal file
BIN
mobile/openapi/test/model_type_test.dart
generated
Normal file
Binary file not shown.
BIN
mobile/openapi/test/recognition_config_test.dart
generated
Normal file
BIN
mobile/openapi/test/recognition_config_test.dart
generated
Normal file
Binary file not shown.
Binary file not shown.
|
|
@ -5354,6 +5354,34 @@
|
|||
],
|
||||
"type": "object"
|
||||
},
|
||||
"CLIPConfig": {
|
||||
"properties": {
|
||||
"enabled": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"mode": {
|
||||
"$ref": "#/components/schemas/CLIPMode"
|
||||
},
|
||||
"modelName": {
|
||||
"type": "string"
|
||||
},
|
||||
"modelType": {
|
||||
"$ref": "#/components/schemas/ModelType"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"enabled",
|
||||
"modelName"
|
||||
],
|
||||
"type": "object"
|
||||
},
|
||||
"CLIPMode": {
|
||||
"enum": [
|
||||
"vision",
|
||||
"text"
|
||||
],
|
||||
"type": "string"
|
||||
},
|
||||
"ChangePasswordDto": {
|
||||
"properties": {
|
||||
"newPassword": {
|
||||
|
|
@ -5432,6 +5460,28 @@
|
|||
],
|
||||
"type": "object"
|
||||
},
|
||||
"ClassificationConfig": {
|
||||
"properties": {
|
||||
"enabled": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"minScore": {
|
||||
"type": "integer"
|
||||
},
|
||||
"modelName": {
|
||||
"type": "string"
|
||||
},
|
||||
"modelType": {
|
||||
"$ref": "#/components/schemas/ModelType"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"minScore",
|
||||
"enabled",
|
||||
"modelName"
|
||||
],
|
||||
"type": "object"
|
||||
},
|
||||
"CreateAlbumDto": {
|
||||
"properties": {
|
||||
"albumName": {
|
||||
|
|
@ -6144,6 +6194,14 @@
|
|||
],
|
||||
"type": "object"
|
||||
},
|
||||
"ModelType": {
|
||||
"enum": [
|
||||
"image-classification",
|
||||
"facial-recognition",
|
||||
"clip"
|
||||
],
|
||||
"type": "string"
|
||||
},
|
||||
"OAuthCallbackDto": {
|
||||
"properties": {
|
||||
"url": {
|
||||
|
|
@ -6323,6 +6381,32 @@
|
|||
],
|
||||
"type": "object"
|
||||
},
|
||||
"RecognitionConfig": {
|
||||
"properties": {
|
||||
"enabled": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"maxDistance": {
|
||||
"type": "integer"
|
||||
},
|
||||
"minScore": {
|
||||
"type": "integer"
|
||||
},
|
||||
"modelName": {
|
||||
"type": "string"
|
||||
},
|
||||
"modelType": {
|
||||
"$ref": "#/components/schemas/ModelType"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"minScore",
|
||||
"maxDistance",
|
||||
"enabled",
|
||||
"modelName"
|
||||
],
|
||||
"type": "object"
|
||||
},
|
||||
"SearchAlbumResponseDto": {
|
||||
"properties": {
|
||||
"count": {
|
||||
|
|
@ -6968,17 +7052,17 @@
|
|||
},
|
||||
"SystemConfigMachineLearningDto": {
|
||||
"properties": {
|
||||
"clipEncodeEnabled": {
|
||||
"type": "boolean"
|
||||
"classification": {
|
||||
"$ref": "#/components/schemas/ClassificationConfig"
|
||||
},
|
||||
"clip": {
|
||||
"$ref": "#/components/schemas/CLIPConfig"
|
||||
},
|
||||
"enabled": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"facialRecognitionEnabled": {
|
||||
"type": "boolean"
|
||||
},
|
||||
"tagImageEnabled": {
|
||||
"type": "boolean"
|
||||
"facialRecognition": {
|
||||
"$ref": "#/components/schemas/RecognitionConfig"
|
||||
},
|
||||
"url": {
|
||||
"type": "string"
|
||||
|
|
@ -6987,9 +7071,9 @@
|
|||
"required": [
|
||||
"enabled",
|
||||
"url",
|
||||
"clipEncodeEnabled",
|
||||
"facialRecognitionEnabled",
|
||||
"tagImageEnabled"
|
||||
"classification",
|
||||
"clip",
|
||||
"facialRecognition"
|
||||
],
|
||||
"type": "object"
|
||||
},
|
||||
|
|
|
|||
|
|
@ -115,6 +115,7 @@ describe(FacialRecognitionService.name, () => {
|
|||
personMock = newPersonRepositoryMock();
|
||||
searchMock = newSearchRepositoryMock();
|
||||
storageMock = newStorageRepositoryMock();
|
||||
configMock = newSystemConfigRepositoryMock();
|
||||
|
||||
mediaMock.crop.mockResolvedValue(croppedFace);
|
||||
|
||||
|
|
@ -179,9 +180,18 @@ describe(FacialRecognitionService.name, () => {
|
|||
machineLearningMock.detectFaces.mockResolvedValue([]);
|
||||
assetMock.getByIds.mockResolvedValue([assetStub.image]);
|
||||
await sut.handleRecognizeFaces({ id: assetStub.image.id });
|
||||
expect(machineLearningMock.detectFaces).toHaveBeenCalledWith('http://immich-machine-learning:3003', {
|
||||
imagePath: assetStub.image.resizePath,
|
||||
});
|
||||
expect(machineLearningMock.detectFaces).toHaveBeenCalledWith(
|
||||
'http://immich-machine-learning:3003',
|
||||
{
|
||||
imagePath: assetStub.image.resizePath,
|
||||
},
|
||||
{
|
||||
enabled: true,
|
||||
maxDistance: 0.6,
|
||||
minScore: 0.7,
|
||||
modelName: 'buffalo_l',
|
||||
},
|
||||
);
|
||||
expect(faceMock.create).not.toHaveBeenCalled();
|
||||
expect(jobMock.queue).not.toHaveBeenCalled();
|
||||
});
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ export class FacialRecognitionService {
|
|||
|
||||
async handleQueueRecognizeFaces({ force }: IBaseJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.facialRecognitionEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.facialRecognition.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -59,7 +59,7 @@ export class FacialRecognitionService {
|
|||
|
||||
async handleRecognizeFaces({ id }: IEntityJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.facialRecognitionEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.facialRecognition.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -68,7 +68,11 @@ export class FacialRecognitionService {
|
|||
return false;
|
||||
}
|
||||
|
||||
const faces = await this.machineLearning.detectFaces(machineLearning.url, { imagePath: asset.resizePath });
|
||||
const faces = await this.machineLearning.detectFaces(
|
||||
machineLearning.url,
|
||||
{ imagePath: asset.resizePath },
|
||||
machineLearning.facialRecognition,
|
||||
);
|
||||
|
||||
this.logger.debug(`${faces.length} faces detected in ${asset.resizePath}`);
|
||||
this.logger.verbose(faces.map((face) => ({ ...face, embedding: `float[${face.embedding.length}]` })));
|
||||
|
|
@ -80,7 +84,7 @@ export class FacialRecognitionService {
|
|||
|
||||
// try to find a matching face and link to the associated person
|
||||
// The closer to 0, the better the match. Range is from 0 to 2
|
||||
if (faceSearchResult.total && faceSearchResult.distances[0] < 0.6) {
|
||||
if (faceSearchResult.total && faceSearchResult.distances[0] <= machineLearning.facialRecognition.maxDistance) {
|
||||
this.logger.verbose(`Match face with distance ${faceSearchResult.distances[0]}`);
|
||||
personId = faceSearchResult.items[0].personId;
|
||||
}
|
||||
|
|
@ -115,7 +119,7 @@ export class FacialRecognitionService {
|
|||
|
||||
async handleGenerateFaceThumbnail(data: IFaceThumbnailJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.facialRecognitionEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.facialRecognition.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -86,6 +86,7 @@ export interface ISearchRepository {
|
|||
deleteAssets(ids: string[]): Promise<void>;
|
||||
deleteFaces(ids: string[]): Promise<void>;
|
||||
deleteAllFaces(): Promise<number>;
|
||||
updateCLIPField(num_dim: number): Promise<void>;
|
||||
|
||||
searchAlbums(query: string, filters: SearchFilter): Promise<SearchResult<AlbumEntity>>;
|
||||
searchAssets(query: string, filters: SearchFilter): Promise<SearchResult<AssetEntity>>;
|
||||
|
|
|
|||
|
|
@ -121,15 +121,18 @@ export class SearchService {
|
|||
await this.configCore.requireFeature(FeatureFlag.SEARCH);
|
||||
|
||||
const query = dto.q || dto.query || '*';
|
||||
const hasClip = machineLearning.enabled && machineLearning.clipEncodeEnabled;
|
||||
const hasClip = machineLearning.enabled && machineLearning.clip.enabled;
|
||||
const strategy = dto.clip && hasClip ? SearchStrategy.CLIP : SearchStrategy.TEXT;
|
||||
const filters = { userId: authUser.id, ...dto };
|
||||
|
||||
let assets: SearchResult<AssetEntity>;
|
||||
switch (strategy) {
|
||||
case SearchStrategy.CLIP:
|
||||
const clip = await this.machineLearning.encodeText(machineLearning.url, query);
|
||||
assets = await this.searchRepository.vectorSearch(clip, filters);
|
||||
const {
|
||||
machineLearning: { clip },
|
||||
} = await this.configCore.getConfig();
|
||||
const embedding = await this.machineLearning.encodeText(machineLearning.url, { text: query }, clip);
|
||||
assets = await this.searchRepository.vectorSearch(embedding, filters);
|
||||
break;
|
||||
case SearchStrategy.TEXT:
|
||||
default:
|
||||
|
|
|
|||
1
server/src/domain/smart-info/dto/index.ts
Normal file
1
server/src/domain/smart-info/dto/index.ts
Normal file
|
|
@ -0,0 +1 @@
|
|||
export * from './model-config.dto';
|
||||
50
server/src/domain/smart-info/dto/model-config.dto.ts
Normal file
50
server/src/domain/smart-info/dto/model-config.dto.ts
Normal file
|
|
@ -0,0 +1,50 @@
|
|||
import { ApiProperty } from '@nestjs/swagger';
|
||||
import { Type } from 'class-transformer';
|
||||
import { IsBoolean, IsEnum, IsNotEmpty, IsNumber, IsOptional, IsString, Max, Min } from 'class-validator';
|
||||
import { CLIPMode, ModelType } from '../machine-learning.interface';
|
||||
|
||||
export class ModelConfig {
|
||||
@IsBoolean()
|
||||
enabled!: boolean;
|
||||
|
||||
@IsString()
|
||||
@IsNotEmpty()
|
||||
modelName!: string;
|
||||
|
||||
@IsEnum(ModelType)
|
||||
@IsOptional()
|
||||
@ApiProperty({ enumName: 'ModelType', enum: ModelType })
|
||||
modelType?: ModelType;
|
||||
}
|
||||
|
||||
export class ClassificationConfig extends ModelConfig {
|
||||
@IsNumber()
|
||||
@Min(0)
|
||||
@Max(1)
|
||||
@Type(() => Number)
|
||||
@ApiProperty({ type: 'integer' })
|
||||
minScore!: number;
|
||||
}
|
||||
|
||||
export class CLIPConfig extends ModelConfig {
|
||||
@IsEnum(CLIPMode)
|
||||
@IsOptional()
|
||||
@ApiProperty({ enumName: 'CLIPMode', enum: CLIPMode })
|
||||
mode?: CLIPMode;
|
||||
}
|
||||
|
||||
export class RecognitionConfig extends ModelConfig {
|
||||
@IsNumber()
|
||||
@Min(0)
|
||||
@Max(1)
|
||||
@Type(() => Number)
|
||||
@ApiProperty({ type: 'integer' })
|
||||
minScore!: number;
|
||||
|
||||
@IsNumber()
|
||||
@Min(0)
|
||||
@Max(2)
|
||||
@Type(() => Number)
|
||||
@ApiProperty({ type: 'integer' })
|
||||
maxDistance!: number;
|
||||
}
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
export * from './dto';
|
||||
export * from './machine-learning.interface';
|
||||
export * from './smart-info.repository';
|
||||
export * from './smart-info.service';
|
||||
|
|
|
|||
|
|
@ -1,9 +1,15 @@
|
|||
import { ClassificationConfig, CLIPConfig, RecognitionConfig } from './dto';
|
||||
|
||||
export const IMachineLearningRepository = 'IMachineLearningRepository';
|
||||
|
||||
export interface MachineLearningInput {
|
||||
export interface VisionModelInput {
|
||||
imagePath: string;
|
||||
}
|
||||
|
||||
export interface TextModelInput {
|
||||
text: string;
|
||||
}
|
||||
|
||||
export interface BoundingBox {
|
||||
x1: number;
|
||||
y1: number;
|
||||
|
|
@ -19,9 +25,20 @@ export interface DetectFaceResult {
|
|||
embedding: number[];
|
||||
}
|
||||
|
||||
export interface IMachineLearningRepository {
|
||||
classifyImage(url: string, input: MachineLearningInput): Promise<string[]>;
|
||||
encodeImage(url: string, input: MachineLearningInput): Promise<number[]>;
|
||||
encodeText(url: string, input: string): Promise<number[]>;
|
||||
detectFaces(url: string, input: MachineLearningInput): Promise<DetectFaceResult[]>;
|
||||
export enum ModelType {
|
||||
IMAGE_CLASSIFICATION = 'image-classification',
|
||||
FACIAL_RECOGNITION = 'facial-recognition',
|
||||
CLIP = 'clip',
|
||||
}
|
||||
|
||||
export enum CLIPMode {
|
||||
VISION = 'vision',
|
||||
TEXT = 'text',
|
||||
}
|
||||
|
||||
export interface IMachineLearningRepository {
|
||||
classifyImage(url: string, input: VisionModelInput, config: ClassificationConfig): Promise<string[]>;
|
||||
encodeImage(url: string, input: VisionModelInput, config: CLIPConfig): Promise<number[]>;
|
||||
encodeText(url: string, input: TextModelInput, config: CLIPConfig): Promise<number[]>;
|
||||
detectFaces(url: string, input: VisionModelInput, config: RecognitionConfig): Promise<DetectFaceResult[]>;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -84,9 +84,13 @@ describe(SmartInfoService.name, () => {
|
|||
|
||||
await sut.handleClassifyImage({ id: asset.id });
|
||||
|
||||
expect(machineMock.classifyImage).toHaveBeenCalledWith('http://immich-machine-learning:3003', {
|
||||
imagePath: 'path/to/resize.ext',
|
||||
});
|
||||
expect(machineMock.classifyImage).toHaveBeenCalledWith(
|
||||
'http://immich-machine-learning:3003',
|
||||
{
|
||||
imagePath: 'path/to/resize.ext',
|
||||
},
|
||||
{ enabled: true, minScore: 0.9, modelName: 'microsoft/resnet-50' },
|
||||
);
|
||||
expect(smartMock.upsert).toHaveBeenCalledWith({
|
||||
assetId: 'asset-1',
|
||||
tags: ['tag1', 'tag2', 'tag3'],
|
||||
|
|
@ -141,13 +145,16 @@ describe(SmartInfoService.name, () => {
|
|||
});
|
||||
|
||||
it('should save the returned objects', async () => {
|
||||
smartMock.upsert.mockResolvedValue();
|
||||
machineMock.encodeImage.mockResolvedValue([0.01, 0.02, 0.03]);
|
||||
|
||||
await sut.handleEncodeClip({ id: asset.id });
|
||||
|
||||
expect(machineMock.encodeImage).toHaveBeenCalledWith('http://immich-machine-learning:3003', {
|
||||
imagePath: 'path/to/resize.ext',
|
||||
});
|
||||
expect(machineMock.encodeImage).toHaveBeenCalledWith(
|
||||
'http://immich-machine-learning:3003',
|
||||
{ imagePath: 'path/to/resize.ext' },
|
||||
{ enabled: true, modelName: 'ViT-B-32::openai' },
|
||||
);
|
||||
expect(smartMock.upsert).toHaveBeenCalledWith({
|
||||
assetId: 'asset-1',
|
||||
clipEmbedding: [0.01, 0.02, 0.03],
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ export class SmartInfoService {
|
|||
|
||||
async handleQueueObjectTagging({ force }: IBaseJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.tagImageEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.classification.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -43,7 +43,7 @@ export class SmartInfoService {
|
|||
|
||||
async handleClassifyImage({ id }: IEntityJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.tagImageEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.classification.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -52,7 +52,11 @@ export class SmartInfoService {
|
|||
return false;
|
||||
}
|
||||
|
||||
const tags = await this.machineLearning.classifyImage(machineLearning.url, { imagePath: asset.resizePath });
|
||||
const tags = await this.machineLearning.classifyImage(
|
||||
machineLearning.url,
|
||||
{ imagePath: asset.resizePath },
|
||||
machineLearning.classification,
|
||||
);
|
||||
await this.repository.upsert({ assetId: asset.id, tags });
|
||||
|
||||
return true;
|
||||
|
|
@ -60,7 +64,7 @@ export class SmartInfoService {
|
|||
|
||||
async handleQueueEncodeClip({ force }: IBaseJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.clipEncodeEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.clip.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -81,7 +85,7 @@ export class SmartInfoService {
|
|||
|
||||
async handleEncodeClip({ id }: IEntityJob) {
|
||||
const { machineLearning } = await this.configCore.getConfig();
|
||||
if (!machineLearning.enabled || !machineLearning.clipEncodeEnabled) {
|
||||
if (!machineLearning.enabled || !machineLearning.clip.enabled) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -90,7 +94,12 @@ export class SmartInfoService {
|
|||
return false;
|
||||
}
|
||||
|
||||
const clipEmbedding = await this.machineLearning.encodeImage(machineLearning.url, { imagePath: asset.resizePath });
|
||||
const clipEmbedding = await this.machineLearning.encodeImage(
|
||||
machineLearning.url,
|
||||
{ imagePath: asset.resizePath },
|
||||
machineLearning.clip,
|
||||
);
|
||||
|
||||
await this.repository.upsert({ assetId: asset.id, clipEmbedding: clipEmbedding });
|
||||
|
||||
return true;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
import { IsBoolean, IsUrl, ValidateIf } from 'class-validator';
|
||||
import { ClassificationConfig, CLIPConfig, RecognitionConfig } from '@app/domain';
|
||||
import { Type } from 'class-transformer';
|
||||
import { IsBoolean, IsObject, IsUrl, ValidateIf, ValidateNested } from 'class-validator';
|
||||
|
||||
export class SystemConfigMachineLearningDto {
|
||||
@IsBoolean()
|
||||
|
|
@ -8,12 +10,18 @@ export class SystemConfigMachineLearningDto {
|
|||
@ValidateIf((dto) => dto.enabled)
|
||||
url!: string;
|
||||
|
||||
@IsBoolean()
|
||||
clipEncodeEnabled!: boolean;
|
||||
@Type(() => ClassificationConfig)
|
||||
@ValidateNested()
|
||||
@IsObject()
|
||||
classification!: ClassificationConfig;
|
||||
|
||||
@IsBoolean()
|
||||
facialRecognitionEnabled!: boolean;
|
||||
@Type(() => CLIPConfig)
|
||||
@ValidateNested()
|
||||
@IsObject()
|
||||
clip!: CLIPConfig;
|
||||
|
||||
@IsBoolean()
|
||||
tagImageEnabled!: boolean;
|
||||
@Type(() => RecognitionConfig)
|
||||
@ValidateNested()
|
||||
@IsObject()
|
||||
facialRecognition!: RecognitionConfig;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -47,12 +47,25 @@ export const defaults = Object.freeze<SystemConfig>({
|
|||
[QueueName.THUMBNAIL_GENERATION]: { concurrency: 5 },
|
||||
[QueueName.VIDEO_CONVERSION]: { concurrency: 1 },
|
||||
},
|
||||
|
||||
machineLearning: {
|
||||
enabled: process.env.IMMICH_MACHINE_LEARNING_ENABLED !== 'false',
|
||||
url: process.env.IMMICH_MACHINE_LEARNING_URL || 'http://immich-machine-learning:3003',
|
||||
facialRecognitionEnabled: true,
|
||||
tagImageEnabled: true,
|
||||
clipEncodeEnabled: true,
|
||||
classification: {
|
||||
enabled: true,
|
||||
modelName: 'microsoft/resnet-50',
|
||||
minScore: 0.9,
|
||||
},
|
||||
clip: {
|
||||
enabled: true,
|
||||
modelName: 'ViT-B-32::openai',
|
||||
},
|
||||
facialRecognition: {
|
||||
enabled: true,
|
||||
modelName: 'buffalo_l',
|
||||
minScore: 0.7,
|
||||
maxDistance: 0.6,
|
||||
},
|
||||
},
|
||||
oauth: {
|
||||
enabled: false,
|
||||
|
|
@ -143,9 +156,9 @@ export class SystemConfigCore {
|
|||
const mlEnabled = config.machineLearning.enabled;
|
||||
|
||||
return {
|
||||
[FeatureFlag.CLIP_ENCODE]: mlEnabled && config.machineLearning.clipEncodeEnabled,
|
||||
[FeatureFlag.FACIAL_RECOGNITION]: mlEnabled && config.machineLearning.facialRecognitionEnabled,
|
||||
[FeatureFlag.TAG_IMAGE]: mlEnabled && config.machineLearning.tagImageEnabled,
|
||||
[FeatureFlag.CLIP_ENCODE]: mlEnabled && config.machineLearning.clip.enabled,
|
||||
[FeatureFlag.FACIAL_RECOGNITION]: mlEnabled && config.machineLearning.facialRecognition.enabled,
|
||||
[FeatureFlag.TAG_IMAGE]: mlEnabled && config.machineLearning.classification.enabled,
|
||||
[FeatureFlag.SIDECAR]: true,
|
||||
[FeatureFlag.SEARCH]: process.env.TYPESENSE_ENABLED !== 'false',
|
||||
|
||||
|
|
@ -230,7 +243,7 @@ export class SystemConfigCore {
|
|||
_.set(config, key, value);
|
||||
}
|
||||
|
||||
return _.defaultsDeep(config, defaults) as SystemConfig;
|
||||
return plainToClass(SystemConfigDto, _.defaultsDeep(config, defaults));
|
||||
}
|
||||
|
||||
private async loadFromFile(filepath: string, force = false) {
|
||||
|
|
|
|||
|
|
@ -49,9 +49,21 @@ const updatedConfig = Object.freeze<SystemConfig>({
|
|||
machineLearning: {
|
||||
enabled: true,
|
||||
url: 'http://immich-machine-learning:3003',
|
||||
facialRecognitionEnabled: true,
|
||||
tagImageEnabled: true,
|
||||
clipEncodeEnabled: true,
|
||||
classification: {
|
||||
enabled: true,
|
||||
modelName: 'microsoft/resnet-50',
|
||||
minScore: 0.9,
|
||||
},
|
||||
clip: {
|
||||
enabled: true,
|
||||
modelName: 'ViT-B-32::openai',
|
||||
},
|
||||
facialRecognition: {
|
||||
enabled: true,
|
||||
modelName: 'buffalo_l',
|
||||
minScore: 0.7,
|
||||
maxDistance: 0.6,
|
||||
},
|
||||
},
|
||||
oauth: {
|
||||
autoLaunch: true,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import { QueueName } from '@app/domain/job/job.constants';
|
||||
import { QueueName } from '@app/domain';
|
||||
import { Column, Entity, PrimaryColumn } from 'typeorm';
|
||||
|
||||
@Entity('system_config')
|
||||
|
|
@ -39,9 +39,18 @@ export enum SystemConfigKey {
|
|||
|
||||
MACHINE_LEARNING_ENABLED = 'machineLearning.enabled',
|
||||
MACHINE_LEARNING_URL = 'machineLearning.url',
|
||||
MACHINE_LEARNING_FACIAL_RECOGNITION_ENABLED = 'machineLearning.facialRecognitionEnabled',
|
||||
MACHINE_LEARNING_TAG_IMAGE_ENABLED = 'machineLearning.tagImageEnabled',
|
||||
MACHINE_LEARNING_CLIP_ENCODE_ENABLED = 'machineLearning.clipEncodeEnabled',
|
||||
|
||||
MACHINE_LEARNING_CLASSIFICATION_ENABLED = 'machineLearning.classification.enabled',
|
||||
MACHINE_LEARNING_CLASSIFICATION_MODEL_NAME = 'machineLearning.classification.modelName',
|
||||
MACHINE_LEARNING_CLASSIFICATION_MIN_SCORE = 'machineLearning.classification.minScore',
|
||||
|
||||
MACHINE_LEARNING_CLIP_ENABLED = 'machineLearning.clip.enabled',
|
||||
MACHINE_LEARNING_CLIP_MODEL_NAME = 'machineLearning.clip.modelName',
|
||||
|
||||
MACHINE_LEARNING_FACIAL_RECOGNITION_ENABLED = 'machineLearning.facialRecognition.enabled',
|
||||
MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL_NAME = 'machineLearning.facialRecognition.modelName',
|
||||
MACHINE_LEARNING_FACIAL_RECOGNITION_MIN_SCORE = 'machineLearning.facialRecognition.minScore',
|
||||
MACHINE_LEARNING_FACIAL_RECOGNITION_MAX_DISTANCE = 'machineLearning.facialRecognition.maxDistance',
|
||||
|
||||
OAUTH_ENABLED = 'oauth.enabled',
|
||||
OAUTH_ISSUER_URL = 'oauth.issuerUrl',
|
||||
|
|
@ -114,9 +123,21 @@ export interface SystemConfig {
|
|||
machineLearning: {
|
||||
enabled: boolean;
|
||||
url: string;
|
||||
clipEncodeEnabled: boolean;
|
||||
facialRecognitionEnabled: boolean;
|
||||
tagImageEnabled: boolean;
|
||||
classification: {
|
||||
enabled: boolean;
|
||||
modelName: string;
|
||||
minScore: number;
|
||||
};
|
||||
clip: {
|
||||
enabled: boolean;
|
||||
modelName: string;
|
||||
};
|
||||
facialRecognition: {
|
||||
enabled: boolean;
|
||||
modelName: string;
|
||||
minScore: number;
|
||||
maxDistance: number;
|
||||
};
|
||||
};
|
||||
oauth: {
|
||||
enabled: boolean;
|
||||
|
|
|
|||
|
|
@ -0,0 +1,25 @@
|
|||
import { MigrationInterface, QueryRunner } from "typeorm"
|
||||
|
||||
export class RenameMLEnableFlags1693236627291 implements MigrationInterface {
|
||||
public async up(queryRunner: QueryRunner): Promise<void> {
|
||||
await queryRunner.query(`
|
||||
UPDATE system_config SET key = CASE
|
||||
WHEN key = 'ffmpeg.classificationEnabled' THEN 'ffmpeg.classification.enabled'
|
||||
WHEN key = 'ffmpeg.clipEnabled' THEN 'ffmpeg.clip.enabled'
|
||||
WHEN key = 'ffmpeg.facialRecognitionEnabled' THEN 'ffmpeg.facialRecognition.enabled'
|
||||
ELSE key
|
||||
END
|
||||
`);
|
||||
}
|
||||
|
||||
public async down(queryRunner: QueryRunner): Promise<void> {
|
||||
await queryRunner.query(`
|
||||
UPDATE system_config SET key = CASE
|
||||
WHEN key = 'ffmpeg.classification.enabled' THEN 'ffmpeg.classificationEnabled'
|
||||
WHEN key = 'ffmpeg.clip.enabled' THEN 'ffmpeg.clipEnabled'
|
||||
WHEN key = 'ffmpeg.facialRecognition.enabled' THEN 'ffmpeg.facialRecognitionEnabled'
|
||||
ELSE key
|
||||
END
|
||||
`);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,29 +1,65 @@
|
|||
import { DetectFaceResult, IMachineLearningRepository, MachineLearningInput } from '@app/domain';
|
||||
import {
|
||||
ClassificationConfig,
|
||||
CLIPConfig,
|
||||
CLIPMode,
|
||||
DetectFaceResult,
|
||||
IMachineLearningRepository,
|
||||
ModelConfig,
|
||||
ModelType,
|
||||
RecognitionConfig,
|
||||
TextModelInput,
|
||||
VisionModelInput,
|
||||
} from '@app/domain';
|
||||
import { Injectable } from '@nestjs/common';
|
||||
import axios from 'axios';
|
||||
import { createReadStream } from 'fs';
|
||||
|
||||
const client = axios.create();
|
||||
import { readFile } from 'fs/promises';
|
||||
|
||||
@Injectable()
|
||||
export class MachineLearningRepository implements IMachineLearningRepository {
|
||||
private post<T>(input: MachineLearningInput, endpoint: string): Promise<T> {
|
||||
return client.post<T>(endpoint, createReadStream(input.imagePath)).then((res) => res.data);
|
||||
private async post<T>(url: string, input: TextModelInput | VisionModelInput, config: ModelConfig): Promise<T> {
|
||||
const formData = await this.getFormData(input, config);
|
||||
const res = await fetch(`${url}/predict`, { method: 'POST', body: formData });
|
||||
return res.json();
|
||||
}
|
||||
|
||||
classifyImage(url: string, input: MachineLearningInput): Promise<string[]> {
|
||||
return this.post<string[]>(input, `${url}/image-classifier/tag-image`);
|
||||
classifyImage(url: string, input: VisionModelInput, config: ClassificationConfig): Promise<string[]> {
|
||||
return this.post<string[]>(url, input, { ...config, modelType: ModelType.IMAGE_CLASSIFICATION });
|
||||
}
|
||||
|
||||
detectFaces(url: string, input: MachineLearningInput): Promise<DetectFaceResult[]> {
|
||||
return this.post<DetectFaceResult[]>(input, `${url}/facial-recognition/detect-faces`);
|
||||
detectFaces(url: string, input: VisionModelInput, config: RecognitionConfig): Promise<DetectFaceResult[]> {
|
||||
return this.post<DetectFaceResult[]>(url, input, { ...config, modelType: ModelType.FACIAL_RECOGNITION });
|
||||
}
|
||||
|
||||
encodeImage(url: string, input: MachineLearningInput): Promise<number[]> {
|
||||
return this.post<number[]>(input, `${url}/sentence-transformer/encode-image`);
|
||||
encodeImage(url: string, input: VisionModelInput, config: CLIPConfig): Promise<number[]> {
|
||||
return this.post<number[]>(url, input, {
|
||||
...config,
|
||||
modelType: ModelType.CLIP,
|
||||
mode: CLIPMode.VISION,
|
||||
} as CLIPConfig);
|
||||
}
|
||||
|
||||
encodeText(url: string, input: string): Promise<number[]> {
|
||||
return client.post<number[]>(`${url}/sentence-transformer/encode-text`, { text: input }).then((res) => res.data);
|
||||
encodeText(url: string, input: TextModelInput, config: CLIPConfig): Promise<number[]> {
|
||||
return this.post<number[]>(url, input, { ...config, modelType: ModelType.CLIP, mode: CLIPMode.TEXT } as CLIPConfig);
|
||||
}
|
||||
|
||||
async getFormData(input: TextModelInput | VisionModelInput, config: ModelConfig): Promise<FormData> {
|
||||
const formData = new FormData();
|
||||
const { modelName, modelType, ...options } = config;
|
||||
|
||||
formData.append('modelName', modelName);
|
||||
if (modelType) {
|
||||
formData.append('modelType', modelType);
|
||||
}
|
||||
if (options) {
|
||||
formData.append('options', JSON.stringify(options));
|
||||
}
|
||||
if ('imagePath' in input) {
|
||||
formData.append('image', new Blob([await readFile(input.imagePath)]));
|
||||
} else if ('text' in input) {
|
||||
formData.append('text', input.text);
|
||||
} else {
|
||||
throw new Error('Invalid input');
|
||||
}
|
||||
|
||||
return formData;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -52,6 +52,8 @@ export class TypesenseRepository implements ISearchRepository {
|
|||
private logger = new Logger(TypesenseRepository.name);
|
||||
|
||||
private _client: Client | null = null;
|
||||
private _updateCLIPLock = false;
|
||||
|
||||
private get client(): Client {
|
||||
if (!this._client) {
|
||||
throw new Error('Typesense client not available (no apiKey was provided)');
|
||||
|
|
@ -141,7 +143,7 @@ export class TypesenseRepository implements ISearchRepository {
|
|||
await this.updateAlias(collection);
|
||||
}
|
||||
} catch (error: any) {
|
||||
this.handleError(error);
|
||||
await this.handleError(error);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -221,6 +223,30 @@ export class TypesenseRepository implements ISearchRepository {
|
|||
return records.num_deleted;
|
||||
}
|
||||
|
||||
async deleteAllAssets(): Promise<number> {
|
||||
const records = await this.client.collections(assetSchema.name).documents().delete({ filter_by: 'ownerId:!=null' });
|
||||
return records.num_deleted;
|
||||
}
|
||||
|
||||
async updateCLIPField(num_dim: number): Promise<void> {
|
||||
const clipField = assetSchema.fields?.find((field) => field.name === 'smartInfo.clipEmbedding');
|
||||
if (clipField && !this._updateCLIPLock) {
|
||||
try {
|
||||
this._updateCLIPLock = true;
|
||||
clipField.num_dim = num_dim;
|
||||
await this.deleteAllAssets();
|
||||
await this.client
|
||||
.collections(assetSchema.name)
|
||||
.update({ fields: [{ name: 'smartInfo.clipEmbedding', drop: true } as any, clipField] });
|
||||
this.logger.log(`Successfully updated CLIP dimensions to ${num_dim}`);
|
||||
} catch (err: any) {
|
||||
this.logger.error(`Error while updating CLIP field: ${err.message}`);
|
||||
} finally {
|
||||
this._updateCLIPLock = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async delete(collection: SearchCollection, ids: string[]): Promise<void> {
|
||||
await this.client
|
||||
.collections(schemaMap[collection].name)
|
||||
|
|
@ -326,21 +352,34 @@ export class TypesenseRepository implements ISearchRepository {
|
|||
} as SearchResult<T>;
|
||||
}
|
||||
|
||||
private handleError(error: any) {
|
||||
private async handleError(error: any) {
|
||||
this.logger.error('Unable to index documents');
|
||||
const results = error.importResults || [];
|
||||
let dimsChanged = false;
|
||||
for (const result of results) {
|
||||
try {
|
||||
result.document = JSON.parse(result.document);
|
||||
if (result.error.includes('Field `smartInfo.clipEmbedding` must have')) {
|
||||
dimsChanged = true;
|
||||
this.logger.warn(
|
||||
`CLIP embedding dimensions have changed, now ${result.document.smartInfo.clipEmbedding.length} dims. Updating schema...`,
|
||||
);
|
||||
await this.updateCLIPField(result.document.smartInfo.clipEmbedding.length);
|
||||
break;
|
||||
}
|
||||
|
||||
if (result.document?.smartInfo?.clipEmbedding) {
|
||||
result.document.smartInfo.clipEmbedding = '<truncated>';
|
||||
}
|
||||
} catch {}
|
||||
} catch (err: any) {
|
||||
this.logger.error(`Error while updating CLIP field: ${(err.message, err.stack)}`);
|
||||
}
|
||||
}
|
||||
|
||||
this.logger.verbose(JSON.stringify(results, null, 2));
|
||||
if (!dimsChanged) {
|
||||
this.logger.log(JSON.stringify(results, null, 2));
|
||||
}
|
||||
}
|
||||
|
||||
private async updateAlias(collection: SearchCollection) {
|
||||
const schema = schemaMap[collection];
|
||||
const alias = await this.client
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ export const newSearchRepositoryMock = (): jest.Mocked<ISearchRepository> => {
|
|||
deleteAssets: jest.fn(),
|
||||
deleteFaces: jest.fn(),
|
||||
deleteAllFaces: jest.fn(),
|
||||
updateCLIPField: jest.fn(),
|
||||
searchAssets: jest.fn(),
|
||||
searchAlbums: jest.fn(),
|
||||
vectorSearch: jest.fn(),
|
||||
|
|
|
|||
154
web/src/api/open-api/api.ts
generated
154
web/src/api/open-api/api.ts
generated
|
|
@ -862,6 +862,53 @@ export interface BulkIdsDto {
|
|||
*/
|
||||
'ids': Array<string>;
|
||||
}
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
* @interface CLIPConfig
|
||||
*/
|
||||
export interface CLIPConfig {
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
* @memberof CLIPConfig
|
||||
*/
|
||||
'enabled': boolean;
|
||||
/**
|
||||
*
|
||||
* @type {CLIPMode}
|
||||
* @memberof CLIPConfig
|
||||
*/
|
||||
'mode'?: CLIPMode;
|
||||
/**
|
||||
*
|
||||
* @type {string}
|
||||
* @memberof CLIPConfig
|
||||
*/
|
||||
'modelName': string;
|
||||
/**
|
||||
*
|
||||
* @type {ModelType}
|
||||
* @memberof CLIPConfig
|
||||
*/
|
||||
'modelType'?: ModelType;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
* @enum {string}
|
||||
*/
|
||||
|
||||
export const CLIPMode = {
|
||||
Vision: 'vision',
|
||||
Text: 'text'
|
||||
} as const;
|
||||
|
||||
export type CLIPMode = typeof CLIPMode[keyof typeof CLIPMode];
|
||||
|
||||
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
|
|
@ -951,6 +998,39 @@ export interface CheckExistingAssetsResponseDto {
|
|||
*/
|
||||
'existingIds': Array<string>;
|
||||
}
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
* @interface ClassificationConfig
|
||||
*/
|
||||
export interface ClassificationConfig {
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
* @memberof ClassificationConfig
|
||||
*/
|
||||
'enabled': boolean;
|
||||
/**
|
||||
*
|
||||
* @type {number}
|
||||
* @memberof ClassificationConfig
|
||||
*/
|
||||
'minScore': number;
|
||||
/**
|
||||
*
|
||||
* @type {string}
|
||||
* @memberof ClassificationConfig
|
||||
*/
|
||||
'modelName': string;
|
||||
/**
|
||||
*
|
||||
* @type {ModelType}
|
||||
* @memberof ClassificationConfig
|
||||
*/
|
||||
'modelType'?: ModelType;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
|
|
@ -1766,6 +1846,21 @@ export interface MergePersonDto {
|
|||
*/
|
||||
'ids': Array<string>;
|
||||
}
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
* @enum {string}
|
||||
*/
|
||||
|
||||
export const ModelType = {
|
||||
ImageClassification: 'image-classification',
|
||||
FacialRecognition: 'facial-recognition',
|
||||
Clip: 'clip'
|
||||
} as const;
|
||||
|
||||
export type ModelType = typeof ModelType[keyof typeof ModelType];
|
||||
|
||||
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
|
|
@ -1991,6 +2086,45 @@ export interface QueueStatusDto {
|
|||
*/
|
||||
'isPaused': boolean;
|
||||
}
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
* @interface RecognitionConfig
|
||||
*/
|
||||
export interface RecognitionConfig {
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
* @memberof RecognitionConfig
|
||||
*/
|
||||
'enabled': boolean;
|
||||
/**
|
||||
*
|
||||
* @type {number}
|
||||
* @memberof RecognitionConfig
|
||||
*/
|
||||
'maxDistance': number;
|
||||
/**
|
||||
*
|
||||
* @type {number}
|
||||
* @memberof RecognitionConfig
|
||||
*/
|
||||
'minScore': number;
|
||||
/**
|
||||
*
|
||||
* @type {string}
|
||||
* @memberof RecognitionConfig
|
||||
*/
|
||||
'modelName': string;
|
||||
/**
|
||||
*
|
||||
* @type {ModelType}
|
||||
* @memberof RecognitionConfig
|
||||
*/
|
||||
'modelType'?: ModelType;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
*
|
||||
* @export
|
||||
|
|
@ -2803,10 +2937,16 @@ export interface SystemConfigJobDto {
|
|||
export interface SystemConfigMachineLearningDto {
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
* @type {ClassificationConfig}
|
||||
* @memberof SystemConfigMachineLearningDto
|
||||
*/
|
||||
'clipEncodeEnabled': boolean;
|
||||
'classification': ClassificationConfig;
|
||||
/**
|
||||
*
|
||||
* @type {CLIPConfig}
|
||||
* @memberof SystemConfigMachineLearningDto
|
||||
*/
|
||||
'clip': CLIPConfig;
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
|
|
@ -2815,16 +2955,10 @@ export interface SystemConfigMachineLearningDto {
|
|||
'enabled': boolean;
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
* @type {RecognitionConfig}
|
||||
* @memberof SystemConfigMachineLearningDto
|
||||
*/
|
||||
'facialRecognitionEnabled': boolean;
|
||||
/**
|
||||
*
|
||||
* @type {boolean}
|
||||
* @memberof SystemConfigMachineLearningDto
|
||||
*/
|
||||
'tagImageEnabled': boolean;
|
||||
'facialRecognition': RecognitionConfig;
|
||||
/**
|
||||
*
|
||||
* @type {string}
|
||||
|
|
|
|||
|
|
@ -4,38 +4,45 @@
|
|||
NotificationType,
|
||||
} from '$lib/components/shared-components/notification/notification';
|
||||
import { handleError } from '$lib/utils/handle-error';
|
||||
import { api, SystemConfigDto } from '@api';
|
||||
import { api, SystemConfigMachineLearningDto } from '@api';
|
||||
import { isEqual } from 'lodash-es';
|
||||
import { fade } from 'svelte/transition';
|
||||
import SettingButtonsRow from '../setting-buttons-row.svelte';
|
||||
import SettingInputField, { SettingInputFieldType } from '../setting-input-field.svelte';
|
||||
import SettingSwitch from '../setting-switch.svelte';
|
||||
import SettingAccordion from '../setting-accordion.svelte';
|
||||
import SettingSelect from '../setting-select.svelte';
|
||||
|
||||
export let machineLearningConfig: SystemConfigMachineLearningDto; // this is the config that is being edited
|
||||
export let disabled = false;
|
||||
|
||||
let config: SystemConfigDto;
|
||||
let defaultConfig: SystemConfigDto;
|
||||
let savedConfig: SystemConfigMachineLearningDto;
|
||||
let defaultConfig: SystemConfigMachineLearningDto;
|
||||
|
||||
async function refreshConfig() {
|
||||
[config, defaultConfig] = await Promise.all([
|
||||
api.systemConfigApi.getConfig().then((res) => res.data),
|
||||
api.systemConfigApi.getDefaults().then((res) => res.data),
|
||||
[savedConfig, defaultConfig] = await Promise.all([
|
||||
api.systemConfigApi.getConfig().then((res) => res.data.machineLearning),
|
||||
api.systemConfigApi.getDefaults().then((res) => res.data.machineLearning),
|
||||
]);
|
||||
}
|
||||
|
||||
async function reset() {
|
||||
const { data: resetConfig } = await api.systemConfigApi.getConfig();
|
||||
config = resetConfig;
|
||||
machineLearningConfig = { ...resetConfig.machineLearning };
|
||||
savedConfig = { ...resetConfig.machineLearning };
|
||||
notificationController.show({ message: 'Reset to the last saved settings', type: NotificationType.Info });
|
||||
}
|
||||
|
||||
async function saveSetting() {
|
||||
try {
|
||||
const { data: current } = await api.systemConfigApi.getConfig();
|
||||
await api.systemConfigApi.updateConfig({
|
||||
systemConfigDto: { ...current, machineLearning: config.machineLearning },
|
||||
const result = await api.systemConfigApi.updateConfig({
|
||||
systemConfigDto: { ...current, machineLearning: machineLearningConfig },
|
||||
});
|
||||
await refreshConfig();
|
||||
|
||||
machineLearningConfig = { ...result.data.machineLearning };
|
||||
savedConfig = { ...result.data.machineLearning };
|
||||
|
||||
notificationController.show({ message: 'Settings saved', type: NotificationType.Info });
|
||||
} catch (error) {
|
||||
handleError(error, 'Unable to save settings');
|
||||
|
|
@ -43,10 +50,7 @@
|
|||
}
|
||||
|
||||
async function resetToDefault() {
|
||||
await refreshConfig();
|
||||
const { data: defaults } = await api.systemConfigApi.getDefaults();
|
||||
config = defaults;
|
||||
|
||||
machineLearningConfig = { ...defaultConfig };
|
||||
notificationController.show({ message: 'Reset settings to defaults', type: NotificationType.Info });
|
||||
}
|
||||
</script>
|
||||
|
|
@ -54,52 +58,152 @@
|
|||
<div class="mt-2">
|
||||
{#await refreshConfig() then}
|
||||
<div in:fade={{ duration: 500 }}>
|
||||
<form autocomplete="off" on:submit|preventDefault class="mx-4 flex flex-col gap-4 py-4">
|
||||
<SettingSwitch
|
||||
title="Enabled"
|
||||
subtitle="Use machine learning features"
|
||||
{disabled}
|
||||
bind:checked={config.machineLearning.enabled}
|
||||
/>
|
||||
<form autocomplete="off" on:submit|preventDefault class="mx-4 mt-4">
|
||||
<div class="flex flex-col gap-4">
|
||||
<SettingSwitch
|
||||
title="ENABLED"
|
||||
subtitle="If disabled, all ML features will be disabled regardless of the below settings."
|
||||
{disabled}
|
||||
bind:checked={machineLearningConfig.enabled}
|
||||
/>
|
||||
|
||||
<hr />
|
||||
<hr />
|
||||
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.TEXT}
|
||||
label="URL"
|
||||
desc="URL of machine learning server"
|
||||
bind:value={config.machineLearning.url}
|
||||
required={true}
|
||||
disabled={disabled || !config.machineLearning.enabled}
|
||||
isEdited={!(config.machineLearning.url === config.machineLearning.url)}
|
||||
/>
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.TEXT}
|
||||
label="URL"
|
||||
desc="URL of the machine learning server"
|
||||
bind:value={machineLearningConfig.url}
|
||||
required={true}
|
||||
disabled={disabled || !machineLearningConfig.enabled}
|
||||
isEdited={machineLearningConfig.url !== savedConfig.url}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<SettingSwitch
|
||||
title="SMART SEARCH"
|
||||
subtitle="Extract CLIP embeddings for smart search"
|
||||
bind:checked={config.machineLearning.clipEncodeEnabled}
|
||||
disabled={disabled || !config.machineLearning.enabled}
|
||||
/>
|
||||
<SettingAccordion title="Image Tagging" subtitle="Tag and classify images with object labels">
|
||||
<div class="ml-4 mt-4 flex flex-col gap-4">
|
||||
<SettingSwitch
|
||||
title="ENABLED"
|
||||
subtitle="If disabled, images will not be tagged. This affects the Things section in the Explore page as well as 'm:' searches."
|
||||
bind:checked={machineLearningConfig.classification.enabled}
|
||||
disabled={disabled || !machineLearningConfig.enabled}
|
||||
/>
|
||||
|
||||
<SettingSwitch
|
||||
title="FACIAL RECOGNITION"
|
||||
subtitle="Recognize and group faces in photos"
|
||||
disabled={disabled || !config.machineLearning.enabled}
|
||||
bind:checked={config.machineLearning.facialRecognitionEnabled}
|
||||
/>
|
||||
<hr />
|
||||
|
||||
<SettingSwitch
|
||||
title="IMAGE TAGGING"
|
||||
subtitle="Tag and classify images"
|
||||
disabled={disabled || !config.machineLearning.enabled}
|
||||
bind:checked={config.machineLearning.tagImageEnabled}
|
||||
/>
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.TEXT}
|
||||
label="IMAGE CLASSIFICATION MODEL"
|
||||
bind:value={machineLearningConfig.classification.modelName}
|
||||
required={true}
|
||||
disabled={disabled || !machineLearningConfig.enabled || !machineLearningConfig.classification.enabled}
|
||||
isEdited={machineLearningConfig.classification.modelName !== savedConfig.classification.modelName}
|
||||
>
|
||||
<p slot="desc" class="immich-form-label pb-2 text-sm">
|
||||
The name of an image classification model listed <a
|
||||
href="https://huggingface.co/models?pipeline_tag=image-classification&sort=trending"><u>here</u></a
|
||||
>. It must be tagged with the 'Image Classification' task and must support ONNX conversion.
|
||||
</p>
|
||||
</SettingInputField>
|
||||
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.NUMBER}
|
||||
label="IMAGE CLASSIFICATION THRESHOLD"
|
||||
desc="Minimum confidence score to add a particular object tag. Lower values will add more tags to images, but may result in more false positives. Will not have any effect until the Tag Objects job is re-run."
|
||||
bind:value={machineLearningConfig.classification.minScore}
|
||||
step="0.1"
|
||||
min="0"
|
||||
max="1"
|
||||
disabled={disabled || !machineLearningConfig.enabled || !machineLearningConfig.classification.enabled}
|
||||
isEdited={machineLearningConfig.classification.minScore !== savedConfig.classification.minScore}
|
||||
/>
|
||||
</div>
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingAccordion title="Smart Search" subtitle="Search for images semantically using CLIP embeddings">
|
||||
<div class="ml-4 mt-4 flex flex-col gap-4">
|
||||
<SettingSwitch
|
||||
title="ENABLED"
|
||||
subtitle="If disabled, images will not be encoded for smart search."
|
||||
bind:checked={machineLearningConfig.clip.enabled}
|
||||
disabled={disabled || !machineLearningConfig.enabled}
|
||||
/>
|
||||
|
||||
<hr />
|
||||
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.TEXT}
|
||||
label="CLIP MODEL"
|
||||
bind:value={machineLearningConfig.clip.modelName}
|
||||
required={true}
|
||||
disabled={disabled || !machineLearningConfig.enabled || !machineLearningConfig.clip.enabled}
|
||||
isEdited={machineLearningConfig.clip.modelName !== savedConfig.clip.modelName}
|
||||
>
|
||||
<p slot="desc" class="immich-form-label pb-2 text-sm">
|
||||
The name of a CLIP model listed <a
|
||||
href="https://clip-as-service.jina.ai/user-guides/benchmark/#size-and-efficiency"><u>here</u></a
|
||||
>. Note that you must re-run the 'Encode CLIP' job for all images upon changing a model.
|
||||
</p>
|
||||
</SettingInputField>
|
||||
</div>
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingAccordion title="Facial Recognition" subtitle="Detect, recognize and group faces in images">
|
||||
<div class="ml-4 mt-4 flex flex-col gap-4">
|
||||
<SettingSwitch
|
||||
title="ENABLED"
|
||||
subtitle="If disabled, images will not be encoded for facial recognition and will not populate the People section in the Explore page."
|
||||
bind:checked={machineLearningConfig.facialRecognition.enabled}
|
||||
disabled={disabled || !machineLearningConfig.enabled}
|
||||
/>
|
||||
|
||||
<hr />
|
||||
|
||||
<SettingSelect
|
||||
label="FACIAL RECOGNITION MODEL"
|
||||
desc="Smaller models are faster and use less memory, but perform worse. Note that you must re-run the Recognize Faces job for all images upon changing a model."
|
||||
name="facial-recognition-model"
|
||||
bind:value={machineLearningConfig.facialRecognition.modelName}
|
||||
options={[
|
||||
{ value: 'buffalo_l', text: 'buffalo_l' },
|
||||
{ value: 'buffalo_s', text: 'buffalo_s' },
|
||||
]}
|
||||
disabled={disabled || !machineLearningConfig.enabled || !machineLearningConfig.facialRecognition.enabled}
|
||||
isEdited={machineLearningConfig.facialRecognition.modelName !== savedConfig.facialRecognition.modelName}
|
||||
/>
|
||||
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.NUMBER}
|
||||
label="MIN DETECTION SCORE"
|
||||
desc="Minimum confidence score for a face to be detected from 0-1. Lower values will detect more faces but may result in false positives."
|
||||
bind:value={machineLearningConfig.facialRecognition.minScore}
|
||||
step="0.1"
|
||||
min="0"
|
||||
max="1"
|
||||
disabled={disabled || !machineLearningConfig.enabled || !machineLearningConfig.facialRecognition.enabled}
|
||||
isEdited={machineLearningConfig.facialRecognition.minScore !== savedConfig.facialRecognition.minScore}
|
||||
/>
|
||||
|
||||
<SettingInputField
|
||||
inputType={SettingInputFieldType.NUMBER}
|
||||
label="MAX RECOGNITION DISTANCE"
|
||||
desc="Maximum distance between two faces to be considered the same person, ranging from 0-2. Lowering this can prevent labeling two people as the same person, while raising it can prevent labeling the same person as two different people. Note that it is easier to merge two people than to split one person in two, so err on the side of a lower threshold when possible."
|
||||
bind:value={machineLearningConfig.facialRecognition.maxDistance}
|
||||
step="0.1"
|
||||
min="0"
|
||||
max="2"
|
||||
disabled={disabled || !machineLearningConfig.enabled || !machineLearningConfig.facialRecognition.enabled}
|
||||
isEdited={machineLearningConfig.facialRecognition.maxDistance !==
|
||||
savedConfig.facialRecognition.maxDistance}
|
||||
/>
|
||||
</div>
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingButtonsRow
|
||||
on:reset={reset}
|
||||
on:save={saveSetting}
|
||||
on:reset-to-default={resetToDefault}
|
||||
showResetToDefault={!isEqual(config, defaultConfig)}
|
||||
showResetToDefault={!isEqual(savedConfig, defaultConfig)}
|
||||
{disabled}
|
||||
/>
|
||||
</form>
|
||||
|
|
|
|||
|
|
@ -13,6 +13,9 @@
|
|||
|
||||
export let inputType: SettingInputFieldType;
|
||||
export let value: string | number;
|
||||
export let min = Number.MIN_VALUE.toString();
|
||||
export let max = Number.MAX_VALUE.toString();
|
||||
export let step = '1';
|
||||
export let label = '';
|
||||
export let desc = '';
|
||||
export let required = false;
|
||||
|
|
@ -48,6 +51,8 @@
|
|||
<p class="immich-form-label pb-2 text-sm" id="{label}-desc">
|
||||
{desc}
|
||||
</p>
|
||||
{:else}
|
||||
<slot name="desc" />
|
||||
{/if}
|
||||
|
||||
<input
|
||||
|
|
@ -57,6 +62,9 @@
|
|||
id={label}
|
||||
name={label}
|
||||
type={inputType}
|
||||
{min}
|
||||
{max}
|
||||
{step}
|
||||
{required}
|
||||
{value}
|
||||
on:input={handleInput}
|
||||
|
|
|
|||
|
|
@ -68,6 +68,10 @@
|
|||
<FFmpegSettings disabled={$featureFlags.configFile} ffmpegConfig={configs.ffmpeg} />
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingAccordion title="Machine Learning Settings" subtitle="Manage model settings">
|
||||
<MachineLearningSettings disabled={$featureFlags.configFile} machineLearningConfig={configs.machineLearning} />
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingAccordion
|
||||
title="Job Settings"
|
||||
subtitle="Manage job concurrency"
|
||||
|
|
@ -84,10 +88,6 @@
|
|||
<OAuthSettings disabled={$featureFlags.configFile} oauthConfig={configs.oauth} />
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingAccordion title="Machine Learning" subtitle="Manage machine learning settings">
|
||||
<MachineLearningSettings disabled={$featureFlags.configFile} />
|
||||
</SettingAccordion>
|
||||
|
||||
<SettingAccordion
|
||||
title="Storage Template"
|
||||
subtitle="Manage the folder structure and file name of the upload asset"
|
||||
|
|
|
|||
Loading…
Reference in a new issue