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[mob] Handle indexed files with no embedding data

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This commit is contained in:
Neeraj Gupta 2024-05-10 16:17:40 +05:30
parent eee168837e
commit 38d622825b
3 changed files with 31 additions and 10 deletions
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14
mobile/lib/services/machine_learning/face_ml/face_ml_service.dart
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@ -27,6 +27,7 @@ import "package:photos/models/file/extensions/file_props.dart";
import "package:photos/models/file/file.dart";
import "package:photos/models/file/file_type.dart";
import "package:photos/models/ml/ml_versions.dart";
import "package:photos/service_locator.dart";
import 'package:photos/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart';
import "package:photos/services/machine_learning/face_ml/face_clustering/face_info_for_clustering.dart";
import 'package:photos/services/machine_learning/face_ml/face_detection/detection.dart';
@ -537,7 +538,8 @@ class FaceMlService {
for (final f in chunk) {
fileIds.add(f.uploadedFileID!);
}
final EnteWatch? w = kDebugMode ? EnteWatch("face_em_fetch") : null;
final EnteWatch? w =
flagService.internalUser ? EnteWatch("face_em_fetch") : null;
w?.start();
w?.log('starting remote fetch for ${fileIds.length} files');
final res =
@ -566,6 +568,16 @@ class FaceMlService {
remoteFileIdToVersion[fileMl.fileID] =
fileMl.faceEmbedding.version;
}
if (res.noEmbeddingFileIDs.isNotEmpty) {
_logger.info(
'No embeddings found for ${res.noEmbeddingFileIDs.length} files',
);
for (final fileID in res.noEmbeddingFileIDs) {
faces.add(Face.empty(fileID, error: false));
remoteFileIdToVersion[fileID] = faceMlVersion;
}
}
await FaceMLDataDB.instance.bulkInsertFaces(faces);
w?.logAndReset('stored embeddings');
for (final entry in remoteFileIdToVersion.entries) {

15
mobile/lib/services/machine_learning/file_ml/files_ml_data_response.dart
View file

@ -2,11 +2,18 @@ import 'package:photos/services/machine_learning/file_ml/file_ml.dart';
class FilesMLDataResponse {
final Map<int, FileMl> mlData;
final Set<int> notIndexedFileIds;
final Set<int> fetchErrorFileIds;
// fileIDs that were indexed but they don't contain any meaningful embeddings
// and hence should be discarded for re-indexing
final Set<int> noEmbeddingFileIDs;
// fetchErrorFileIDs are the fileIDs for whom we failed failed to fetch embeddings
// from the storage
final Set<int> fetchErrorFileIDs;
// pendingIndexFileIDs are the fileIDs that were never indexed
final Set<int> pendingIndexFileIDs;
FilesMLDataResponse(
this.mlData, {
required this.notIndexedFileIds,
required this.fetchErrorFileIds,
required this.noEmbeddingFileIDs,
required this.fetchErrorFileIDs,
required this.pendingIndexFileIDs,
});
}

12
mobile/lib/services/machine_learning/file_ml/remote_fileml_service.dart
View file

@ -72,7 +72,8 @@ class RemoteFileMLService {
},
);
final remoteEmb = res.data['embeddings'] as List;
final noFileIds = res.data['noDataFileIDs'] as List;
final pendingIndexFiles = res.data['pendingIndexFileIDs'] as List;
final noEmbeddingFiles = res.data['noEmbeddingFileIDs'] as List;
final errFileIds = res.data['errFileIDs'] as List;
final List<RemoteEmbedding> remoteEmbeddings = <RemoteEmbedding>[];
@ -81,13 +82,14 @@ class RemoteFileMLService {
remoteEmbeddings.add(embedding);
}
final notIndexedFileIds = Set<int>.from(noFileIds.map((x) => x as int));
final fetchErrorFileIds = Set<int>.from(errFileIds.map((x) => x as int));
final fileIDToFileMl = await decryptFileMLData(remoteEmbeddings);
return FilesMLDataResponse(
fileIDToFileMl,
notIndexedFileIds: notIndexedFileIds,
fetchErrorFileIds: fetchErrorFileIds,
noEmbeddingFileIDs:
Set<int>.from(noEmbeddingFiles.map((x) => x as int)),
fetchErrorFileIDs: Set<int>.from(errFileIds.map((x) => x as int)),
pendingIndexFileIDs:
Set<int>.from(pendingIndexFiles.map((x) => x as int)),
);
} catch (e, s) {
_logger.severe("Failed to get embeddings", e, s);