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fix: remove formatting from ml_service

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This commit is contained in:
Prateek Sunal
2025-08-24 02:04:09 +05:30
parent f3a2b2af0c
commit db2d0bb7e9
1 changed files with 38 additions and 49 deletions
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87
mobile/apps/photos/lib/services/machine_learning/ml_service.dart
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@@ -131,8 +131,8 @@ class MLService {
_isRunningML = true;
await sync();
final int unclusteredFacesCount = await mlDataDB
.getUnclusteredFaceCount();
final int unclusteredFacesCount =
await mlDataDB.getUnclusteredFaceCount();
if (unclusteredFacesCount > _kForceClusteringFaceCount) {
_logger.info(
"There are $unclusteredFacesCount unclustered faces, doing clustering first",
@@ -279,8 +279,8 @@ class MLService {
try {
// Get a sense of the total number of faces in the database
final int totalFaces = await mlDataDB.getTotalFaceCount();
final fileIDToCreationTime = await FilesDB.instance
.getFileIDToCreationTime();
final fileIDToCreationTime =
await FilesDB.instance.getFileIDToCreationTime();
final startEmbeddingFetch = DateTime.now();
// read all embeddings
final result = await mlDataDB.getFaceInfoForClustering(
@@ -300,9 +300,8 @@ class MLService {
}
// sort the embeddings based on file creation time, newest first
allFaceInfoForClustering.sort((b, a) {
return fileIDToCreationTime[a.fileID]!.compareTo(
fileIDToCreationTime[b.fileID]!,
);
return fileIDToCreationTime[a.fileID]!
.compareTo(fileIDToCreationTime[b.fileID]!);
});
_logger.info(
'Getting and sorting embeddings took ${DateTime.now().difference(startEmbeddingFetch).inMilliseconds} ms for ${allFaceInfoForClustering.length} embeddings'
@@ -310,8 +309,8 @@ class MLService {
);
// Get the current cluster statistics
final Map<String, (Uint8List, int)> oldClusterSummaries = await mlDataDB
.getAllClusterSummary();
final Map<String, (Uint8List, int)> oldClusterSummaries =
await mlDataDB.getAllClusterSummary();
if (clusterInBuckets) {
const int bucketSize = 10000;
@@ -358,24 +357,22 @@ class MLService {
}
}
final clusteringResult = await FaceClusteringService.instance
.predictLinearIsolate(
faceInfoForClustering.toSet(),
fileIDToCreationTime: fileIDToCreationTime,
offset: offset,
oldClusterSummaries: oldClusterSummaries,
);
final clusteringResult =
await FaceClusteringService.instance.predictLinearIsolate(
faceInfoForClustering.toSet(),
fileIDToCreationTime: fileIDToCreationTime,
offset: offset,
oldClusterSummaries: oldClusterSummaries,
);
if (clusteringResult == null) {
_logger.warning("faceIdToCluster is null");
return;
}
await mlDataDB.updateFaceIdToClusterId(
clusteringResult.newFaceIdToCluster,
);
await mlDataDB.clusterSummaryUpdate(
clusteringResult.newClusterSummaries,
);
await mlDataDB
.updateFaceIdToClusterId(clusteringResult.newFaceIdToCluster);
await mlDataDB
.clusterSummaryUpdate(clusteringResult.newClusterSummaries);
Bus.instance.fire(PeopleChangedEvent());
for (final faceInfo in faceInfoForClustering) {
faceInfo.clusterId ??=
@@ -398,12 +395,12 @@ class MLService {
} else {
final clusterStartTime = DateTime.now();
// Cluster the embeddings using the linear clustering algorithm, returning a map from faceID to clusterID
final clusteringResult = await FaceClusteringService.instance
.predictLinearIsolate(
allFaceInfoForClustering.toSet(),
fileIDToCreationTime: fileIDToCreationTime,
oldClusterSummaries: oldClusterSummaries,
);
final clusteringResult =
await FaceClusteringService.instance.predictLinearIsolate(
allFaceInfoForClustering.toSet(),
fileIDToCreationTime: fileIDToCreationTime,
oldClusterSummaries: oldClusterSummaries,
);
if (clusteringResult == null) {
_logger.warning("faceIdToCluster is null");
return;
@@ -417,22 +414,16 @@ class MLService {
_logger.info(
'Updating ${clusteringResult.newFaceIdToCluster.length} FaceIDs with clusterIDs in the DB',
);
await mlDataDB.updateFaceIdToClusterId(
clusteringResult.newFaceIdToCluster,
);
await mlDataDB.clusterSummaryUpdate(
clusteringResult.newClusterSummaries,
);
await mlDataDB
.updateFaceIdToClusterId(clusteringResult.newFaceIdToCluster);
await mlDataDB
.clusterSummaryUpdate(clusteringResult.newClusterSummaries);
Bus.instance.fire(PeopleChangedEvent());
_logger.info(
'Done updating FaceIDs with clusterIDs in the DB, in '
'${DateTime.now().difference(clusterDoneTime).inSeconds} seconds',
);
_logger.info('Done updating FaceIDs with clusterIDs in the DB, in '
'${DateTime.now().difference(clusterDoneTime).inSeconds} seconds');
}
_logger.info(
'clusterAllImages() finished, in '
'${DateTime.now().difference(clusterAllImagesTime).inSeconds} seconds',
);
_logger.info('clusterAllImages() finished, in '
'${DateTime.now().difference(clusterAllImagesTime).inSeconds} seconds');
} catch (e, s) {
_logger.severe("`clusterAllImages` failed", e, s);
} finally {
@@ -465,8 +456,7 @@ class MLService {
actuallyRanML = result.ranML;
if (!actuallyRanML) return actuallyRanML;
// Prepare storing data on remote
final FileDataEntity dataEntity =
instruction.existingRemoteFileML ??
final FileDataEntity dataEntity = instruction.existingRemoteFileML ??
FileDataEntity.empty(
instruction.file.uploadedFileID!,
DataType.mlData,
@@ -529,9 +519,9 @@ class MLService {
final fileType = instruction.file.fileType;
final bool acceptedIssue =
errorString.contains('ThumbnailRetrievalException') ||
errorString.contains('InvalidImageFormatException') ||
errorString.contains('UnhandledExifOrientation') ||
errorString.contains('FileSizeTooLargeForMobileIndexing');
errorString.contains('InvalidImageFormatException') ||
errorString.contains('UnhandledExifOrientation') ||
errorString.contains('FileSizeTooLargeForMobileIndexing');
if (acceptedIssue) {
_logger.severe(
'$errorString for fileID ${instruction.file.uploadedFileID} (format $format, type $fileType, size $size), storing empty results so indexing does not get stuck',
@@ -592,8 +582,7 @@ class MLService {
}
void _logStatus() {
final String status =
'''
final String status = '''
isInternalUser: ${flagService.internalUser}
Local indexing: ${localSettings.isMLLocalIndexingEnabled}
canRunMLController: $_mlControllerStatus