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Merge branch 'mobile_face' of https://github.com/ente-io/auth into mobile_face

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This commit is contained in:
Neeraj Gupta 2024-05-02 14:22:31 +05:30
commit 6b70c721d4
2 changed files with 59 additions and 48 deletions
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103
mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
View file

@ -481,46 +481,46 @@ class ClusterFeedbackService {
late Map<int, Vector> clusterAvgBigClusters;
final List<(int, double)> suggestionsMean = [];
for (final minimumSize in checkSizes.toSet()) {
// if (smallestPersonClusterSize >= minimumSize) {
clusterAvgBigClusters = await _getUpdateClusterAvg(
allClusterIdsToCountMap,
ignoredClusters,
minClusterSize: minimumSize,
);
w?.log(
'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
);
final List<(int, double)> suggestionsMeanBigClusters =
_calcSuggestionsMean(
clusterAvgBigClusters,
personClusters,
ignoredClusters,
goodMeanDistance,
);
w?.log(
'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
);
for (final suggestion in suggestionsMeanBigClusters) {
// Skip suggestions that have a high overlap with the person's files
final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
.map((faceID) => getFileIdFromFaceId(faceID))
.toSet();
final overlap = personFileIDs.intersection(suggestionSet);
if (overlap.isNotEmpty &&
((overlap.length / suggestionSet.length) > 0.5)) {
await FaceMLDataDB.instance.captureNotPersonFeedback(
personID: p.remoteID,
clusterID: suggestion.$1,
);
continue;
if (smallestPersonClusterSize >= minimumSize) {
clusterAvgBigClusters = await _getUpdateClusterAvg(
allClusterIdsToCountMap,
ignoredClusters,
minClusterSize: minimumSize,
);
w?.log(
'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
);
final List<(int, double)> suggestionsMeanBigClusters =
_calcSuggestionsMean(
clusterAvgBigClusters,
personClusters,
ignoredClusters,
goodMeanDistance,
);
w?.log(
'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
);
for (final suggestion in suggestionsMeanBigClusters) {
// Skip suggestions that have a high overlap with the person's files
final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
.map((faceID) => getFileIdFromFaceId(faceID))
.toSet();
final overlap = personFileIDs.intersection(suggestionSet);
if (overlap.isNotEmpty &&
((overlap.length / suggestionSet.length) > 0.5)) {
await FaceMLDataDB.instance.captureNotPersonFeedback(
personID: p.remoteID,
clusterID: suggestion.$1,
);
continue;
}
suggestionsMean.add(suggestion);
}
if (suggestionsMean.isNotEmpty) {
return suggestionsMean
.map((e) => (e.$1, e.$2, true))
.toList(growable: false);
}
suggestionsMean.add(suggestion);
}
if (suggestionsMean.isNotEmpty) {
return suggestionsMean
.map((e) => (e.$1, e.$2, true))
.toList(growable: false);
// }
}
}
w?.reset();
@ -784,24 +784,31 @@ class ClusterFeedbackService {
Map<int, int>? allClusterIdsToCountMap,
}) {
final Map<int, List<(int, double)>> suggestions = {};
const suggestionMax = 2000;
int suggestionCount = 0;
int comparisons = 0;
final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
for (final otherClusterID in clusterAvg.keys) {
// ignore the cluster that belong to the person or is ignored
if (personClusters.contains(otherClusterID) ||
ignoredClusters.contains(otherClusterID)) {
// ignore the clusters that belong to the person or is ignored
Set<int> otherClusters = clusterAvg.keys.toSet().difference(personClusters);
otherClusters = otherClusters.difference(ignoredClusters);
for (final otherClusterID in otherClusters) {
final Vector? otherAvg = clusterAvg[otherClusterID];
if (otherAvg == null) {
_logger.warning('no avg for othercluster $otherClusterID');
continue;
}
final Vector otherAvg = clusterAvg[otherClusterID]!;
int? nearestPersonCluster;
double? minDistance;
for (final personCluster in personClusters) {
if (clusterAvg[personCluster] == null) {
_logger.info('no avg for cluster $personCluster');
_logger.warning('no avg for personcluster $personCluster');
continue;
}
final Vector avg = clusterAvg[personCluster]!;
final distance = cosineDistanceSIMD(avg, otherAvg);
comparisons++;
if (distance < maxClusterDistance) {
if (minDistance == null || distance < minDistance) {
minDistance = distance;
@ -815,11 +822,13 @@ class ClusterFeedbackService {
.add((otherClusterID, minDistance));
suggestionCount++;
}
if (suggestionCount >= 2000) {
if (suggestionCount >= suggestionMax) {
break;
}
}
w?.log('calculation inside calcSuggestionsMean');
w?.log(
'calculation inside calcSuggestionsMean for ${personClusters.length} person clusters and ${otherClusters.length} other clusters (so ${personClusters.length * otherClusters.length} combinations, $comparisons comparisons made resulted in $suggestionCount suggestions)',
);
if (suggestions.isNotEmpty) {
final List<(int, double)> suggestClusterIds = [];

4
mobile/lib/services/machine_learning/face_ml/person/person_service.dart
View file

@ -114,7 +114,9 @@ class PersonService {
}
bool _shouldUpdateRemotePerson(
PersonData personData, Map<int, Set<String>> dbPersonCluster) {
PersonData personData,
Map<int, Set<String>> dbPersonCluster,
) {
bool result = false;
if ((personData.assigned?.length ?? 0) != dbPersonCluster.length) {
log(