mirror of
https://github.com/ente-io/ente.git
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128 lines
4.0 KiB
Dart
128 lines
4.0 KiB
Dart
import "dart:io" show File, Platform;
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import "package:logging/logging.dart";
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import "package:onnx_dart/onnx_dart.dart";
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import "package:onnxruntime/onnxruntime.dart";
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import "package:photos/services/machine_learning/onnx_env.dart";
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import "package:photos/services/remote_assets_service.dart";
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abstract class MlModel {
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static final Logger isolateLogger = Logger("MlModelInIsolate");
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Logger get logger;
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String get kModelBucketEndpoint => "https://models.ente.io/";
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String get modelRemotePath;
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String get modelName;
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static final bool _usePlatformPlugin = Platform.isAndroid;
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bool get isInitialized =>
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_usePlatformPlugin ? _isNativePluginInitialized : _isFfiInitialized;
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int get sessionAddress =>
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_usePlatformPlugin ? _nativePluginSessionIndex : _ffiSessionAddress;
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// isInitialized is used to check if the model is loaded by the ffi based
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// plugin
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bool _isFfiInitialized = false;
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int _ffiSessionAddress = -1;
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bool _isNativePluginInitialized = false;
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int _nativePluginSessionIndex = -1;
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Future<(String, String)> getModelNameAndPath() async {
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final path =
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await RemoteAssetsService.instance.getAssetPath(modelRemotePath);
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return (modelName, path);
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}
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void storeSessionAddress(int address) {
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if (_usePlatformPlugin) {
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_nativePluginSessionIndex = address;
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_isNativePluginInitialized = true;
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} else {
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_ffiSessionAddress = address;
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_isFfiInitialized = true;
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}
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}
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// Initializes the model.
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// If `useEntePlugin` is set to true, the custom plugin is used for initialization.
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// Note: The custom plugin requires a dedicated isolate for loading the model to ensure thread safety and performance isolation.
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// In contrast, the current FFI-based plugin leverages the session memory address for session management, which does not require a dedicated isolate.
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static Future<int> loadModel(
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String modelName,
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String modelPath,
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) async {
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if (_usePlatformPlugin) {
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return await _loadModelWithPlatformPlugin(modelName, modelPath);
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} else {
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return await _loadModelWithFFI(modelName, modelPath);
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}
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}
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static Future<int> _loadModelWithPlatformPlugin(
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String modelName,
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String modelPath,
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) async {
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final startTime = DateTime.now();
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isolateLogger.info('Initializing $modelName with EntePlugin');
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final OnnxDart plugin = OnnxDart();
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final bool? initResult = await plugin.init(modelName, modelPath);
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if (initResult == null || !initResult) {
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isolateLogger.severe("Failed to initialize $modelName with EntePlugin.");
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throw Exception("Failed to initialize $modelName with EntePlugin.");
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}
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final endTime = DateTime.now();
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isolateLogger.info(
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"$modelName loaded via EntePlugin in ${endTime.difference(startTime).inMilliseconds}ms",
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);
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return 0;
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}
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static Future<int> _loadModelWithFFI(
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String modelName,
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String modelPath,
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) async {
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isolateLogger.info('Initializing $modelName with FFI');
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try {
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final startTime = DateTime.now();
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final sessionOptions = OrtSessionOptions()
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..setInterOpNumThreads(1)
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..setIntraOpNumThreads(1)
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..setSessionGraphOptimizationLevel(
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GraphOptimizationLevel.ortEnableAll,
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);
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final session = OrtSession.fromFile(File(modelPath), sessionOptions);
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final endTime = DateTime.now();
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isolateLogger.info(
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"$modelName loaded with FFI, took: ${endTime.difference(startTime).inMilliseconds}ms",
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);
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return session.address;
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} catch (e) {
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rethrow;
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}
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}
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// TODO: add release method for native plugin
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Future<void> release() async {
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if (_isFfiInitialized) {
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await _releaseModel({'address': _ffiSessionAddress});
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await ONNXEnv.instance.releaseONNX(modelName);
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_isFfiInitialized = false;
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_ffiSessionAddress = 0;
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}
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}
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static Future<void> _releaseModel(Map args) async {
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final address = args['address'] as int;
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if (address == 0) {
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return;
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}
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final session = OrtSession.fromAddress(address);
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session.release();
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return;
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}
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}
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