mirror of https://github.com/immich-app/immich.git
feat(server,ml): remove image tagging (#5903)
* remove image tagging * updated lock * fixed tests, improved logging * be nice * fixed testspull/5904/head
Mert
GitHub
parent
154292242f
commit
092a23fd7f
@ -1,75 +0,0 @@
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from io import BytesIO
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from pathlib import Path
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from typing import Any
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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 import Image
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from transformers import AutoImageProcessor
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from ..config import log
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from ..schemas import ModelType
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from .base import InferenceModel
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class ImageClassifier(InferenceModel):
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_model_type = ModelType.IMAGE_CLASSIFICATION
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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 = 0.9,
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cache_dir: Path | str | None = None,
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**model_kwargs: Any,
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) -> None:
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self.min_score = model_kwargs.pop("minScore", min_score)
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super().__init__(model_name, cache_dir, **model_kwargs)
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def _download(self) -> None:
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snapshot_download(
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cache_dir=self.cache_dir,
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repo_id=self.model_name,
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allow_patterns=["*.bin", "*.json", "*.txt"],
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local_dir=self.cache_dir,
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local_dir_use_symlinks=True,
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)
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def _load(self) -> None:
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processor = AutoImageProcessor.from_pretrained(self.cache_dir, cache_dir=self.cache_dir)
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model_path = self.cache_dir / "model.onnx"
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model_kwargs = {
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"cache_dir": self.cache_dir,
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"provider": self.providers[0],
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"provider_options": self.provider_options[0],
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"session_options": self.sess_options,
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}
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if model_path.exists():
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model = ORTModelForImageClassification.from_pretrained(self.cache_dir, **model_kwargs)
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self.model = pipeline(self.model_type.value, model, feature_extractor=processor)
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else:
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log.info(
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(
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f"ONNX model not found in cache directory for '{self.model_name}'."
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"Exporting optimized model for future use."
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),
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)
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self.sess_options.optimized_model_filepath = model_path.as_posix()
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self.model = pipeline(
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self.model_type.value,
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self.model_name,
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model_kwargs=model_kwargs,
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feature_extractor=processor,
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)
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def _predict(self, image: Image.Image | bytes) -> list[str]:
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if isinstance(image, bytes):
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image = Image.open(BytesIO(image))
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predictions: list[dict[str, Any]] = self.model(image)
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tags = [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score]
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return tags
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def configure(self, **model_kwargs: Any) -> None:
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self.min_score = model_kwargs.pop("minScore", self.min_score)
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File diff suppressed because it is too large
Load Diff
@ -1,18 +0,0 @@
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# openapi.model.ClassificationConfig
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## Load the model package
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```dart
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import 'package:openapi/api.dart';
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```
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## Properties
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**enabled** | **bool** | |
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**minScore** | **int** | |
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**modelName** | **String** | |
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**modelType** | [**ModelType**](ModelType.md) | | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
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@ -1,131 +0,0 @@
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//
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// AUTO-GENERATED FILE, DO NOT MODIFY!
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//
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// @dart=2.12
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// ignore_for_file: unused_element, unused_import
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// ignore_for_file: always_put_required_named_parameters_first
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// ignore_for_file: constant_identifier_names
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// ignore_for_file: lines_longer_than_80_chars
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part of openapi.api;
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class ClassificationConfig {
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/// Returns a new [ClassificationConfig] instance.
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ClassificationConfig({
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required this.enabled,
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required this.minScore,
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required this.modelName,
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this.modelType,
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});
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bool enabled;
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int minScore;
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String modelName;
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///
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/// Please note: This property should have been non-nullable! Since the specification file
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/// does not include a default value (using the "default:" property), however, the generated
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/// source code must fall back to having a nullable type.
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/// Consider adding a "default:" property in the specification file to hide this note.
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///
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ModelType? modelType;
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@override
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bool operator ==(Object other) => identical(this, other) || other is ClassificationConfig &&
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other.enabled == enabled &&
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other.minScore == minScore &&
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other.modelName == modelName &&
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other.modelType == modelType;
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@override
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int get hashCode =>
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// ignore: unnecessary_parenthesis
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(enabled.hashCode) +
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(minScore.hashCode) +
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(modelName.hashCode) +
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(modelType == null ? 0 : modelType!.hashCode);
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@override
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String toString() => 'ClassificationConfig[enabled=$enabled, minScore=$minScore, modelName=$modelName, modelType=$modelType]';
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Map<String, dynamic> toJson() {
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final json = <String, dynamic>{};
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json[r'enabled'] = this.enabled;
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json[r'minScore'] = this.minScore;
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json[r'modelName'] = this.modelName;
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if (this.modelType != null) {
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json[r'modelType'] = this.modelType;
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} else {
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// json[r'modelType'] = null;
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}
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return json;
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}
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/// Returns a new [ClassificationConfig] instance and imports its values from
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/// [value] if it's a [Map], null otherwise.
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// ignore: prefer_constructors_over_static_methods
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static ClassificationConfig? fromJson(dynamic value) {
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if (value is Map) {
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final json = value.cast<String, dynamic>();
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return ClassificationConfig(
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enabled: mapValueOfType<bool>(json, r'enabled')!,
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minScore: mapValueOfType<int>(json, r'minScore')!,
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modelName: mapValueOfType<String>(json, r'modelName')!,
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modelType: ModelType.fromJson(json[r'modelType']),
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);
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}
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return null;
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}
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static List<ClassificationConfig> listFromJson(dynamic json, {bool growable = false,}) {
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final result = <ClassificationConfig>[];
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if (json is List && json.isNotEmpty) {
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for (final row in json) {
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final value = ClassificationConfig.fromJson(row);
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if (value != null) {
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result.add(value);
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}
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}
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}
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return result.toList(growable: growable);
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}
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static Map<String, ClassificationConfig> mapFromJson(dynamic json) {
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final map = <String, ClassificationConfig>{};
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if (json is Map && json.isNotEmpty) {
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json = json.cast<String, dynamic>(); // ignore: parameter_assignments
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for (final entry in json.entries) {
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final value = ClassificationConfig.fromJson(entry.value);
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if (value != null) {
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map[entry.key] = value;
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}
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}
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}
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return map;
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}
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// maps a json object with a list of ClassificationConfig-objects as value to a dart map
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static Map<String, List<ClassificationConfig>> mapListFromJson(dynamic json, {bool growable = false,}) {
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final map = <String, List<ClassificationConfig>>{};
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if (json is Map && json.isNotEmpty) {
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// ignore: parameter_assignments
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json = json.cast<String, dynamic>();
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for (final entry in json.entries) {
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map[entry.key] = ClassificationConfig.listFromJson(entry.value, growable: growable,);
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}
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}
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return map;
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}
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/// The list of required keys that must be present in a JSON.
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static const requiredKeys = <String>{
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'enabled',
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'minScore',
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'modelName',
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};
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}
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@ -1,42 +0,0 @@
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//
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// AUTO-GENERATED FILE, DO NOT MODIFY!
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//
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// @dart=2.12
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// ignore_for_file: unused_element, unused_import
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// ignore_for_file: always_put_required_named_parameters_first
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// ignore_for_file: constant_identifier_names
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// ignore_for_file: lines_longer_than_80_chars
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import 'package:openapi/api.dart';
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import 'package:test/test.dart';
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// tests for ClassificationConfig
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void main() {
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// final instance = ClassificationConfig();
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group('test ClassificationConfig', () {
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// bool enabled
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test('to test the property `enabled`', () async {
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// TODO
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});
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// int minScore
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test('to test the property `minScore`', () async {
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// TODO
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});
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// String modelName
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test('to test the property `modelName`', () async {
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// TODO
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});
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// ModelType modelType
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test('to test the property `modelType`', () async {
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// TODO
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});
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});
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}
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