[mob][photos] Fix breakupCluster not returning cluster summaries

mobile/lib/services/machine_learning/face_ml/face_clustering/face_clustering_service.dart

@@ -45,6 +45,8 @@ class ClusteringResult {
 
   bool get isEmpty => newFaceIdToCluster.isEmpty;
 
+  bool get hasAllResults => newClusterSummaries != null && newClusterIdToFaceIds != null;
+
   ClusteringResult({
     required this.newFaceIdToCluster,
     this.newClusterSummaries,
@@ -127,8 +129,7 @@ class FaceClusteringService {
             break;
         }
       } catch (e, stackTrace) {
-        sendPort
-            .send({'error': e.toString(), 'stackTrace': stackTrace.toString()});
+        sendPort.send({'error': e.toString(), 'stackTrace': stackTrace.toString()});
       }
     });
   }
@@ -256,6 +257,7 @@ class FaceClusteringService {
   Future<ClusteringResult?> predictLinearComputer(
     Map<String, Uint8List> input, {
     Map<int, int>? fileIDToCreationTime,
+    required Map<int, (Uint8List, int)> oldClusterSummaries,
     double distanceThreshold = kRecommendedDistanceThreshold,
   }) async {
     if (input.isEmpty) {
@@ -291,6 +293,7 @@ class FaceClusteringService {
         param: {
           "input": clusteringInput,
           "fileIDToCreationTime": fileIDToCreationTime,
+          "oldClusterSummaries": oldClusterSummaries,
           "distanceThreshold": distanceThreshold,
           "conservativeDistanceThreshold": distanceThreshold - 0.08,
           "useDynamicThreshold": false,
@@ -314,6 +317,7 @@ class FaceClusteringService {
   Future<ClusteringResult> predictCompleteComputer(
     Map<String, Uint8List> input, {
     Map<int, int>? fileIDToCreationTime,
+    required Map<int, (Uint8List, int)> oldClusterSummaries,
     double distanceThreshold = kRecommendedDistanceThreshold,
     double mergeThreshold = 0.30,
   }) async {
@@ -336,6 +340,7 @@ class FaceClusteringService {
         param: {
           "input": input,
           "fileIDToCreationTime": fileIDToCreationTime,
+          "oldClusterSummaries": oldClusterSummaries,
           "distanceThreshold": distanceThreshold,
           "mergeThreshold": mergeThreshold,
         },
@@ -355,6 +360,7 @@ class FaceClusteringService {
   Future<ClusteringResult?> predictWithinClusterComputer(
     Map<String, Uint8List> input, {
     Map<int, int>? fileIDToCreationTime,
+    Map<int, (Uint8List, int)> oldClusterSummaries = const <int, (Uint8List, int)>{},
     double distanceThreshold = kRecommendedDistanceThreshold,
   }) async {
     _logger.info(
@@ -369,6 +375,7 @@ class FaceClusteringService {
         final result = await predictCompleteComputer(
           input,
           fileIDToCreationTime: fileIDToCreationTime,
+          oldClusterSummaries: oldClusterSummaries,
           distanceThreshold: distanceThreshold - 0.08,
           mergeThreshold: mergeThreshold,
         );
@@ -381,6 +388,7 @@ class FaceClusteringService {
         final clusterResult = await predictLinearComputer(
           input,
           fileIDToCreationTime: fileIDToCreationTime,
+          oldClusterSummaries: oldClusterSummaries,
           distanceThreshold: distanceThreshold,
         );
         return clusterResult;
@@ -396,12 +404,10 @@ class FaceClusteringService {
     final input = args['input'] as Set<FaceInfoForClustering>;
     final fileIDToCreationTime = args['fileIDToCreationTime'] as Map<int, int>?;
     final distanceThreshold = args['distanceThreshold'] as double;
-    final conservativeDistanceThreshold =
-        args['conservativeDistanceThreshold'] as double;
+    final conservativeDistanceThreshold = args['conservativeDistanceThreshold'] as double;
     final useDynamicThreshold = args['useDynamicThreshold'] as bool;
     final offset = args['offset'] as int?;
-    final oldClusterSummaries =
-        args['oldClusterSummaries'] as Map<int, (Uint8List, int)>?;
+    final oldClusterSummaries = args['oldClusterSummaries'] as Map<int, (Uint8List, int)>?;
 
     log(
       "[ClusterIsolate] ${DateTime.now()} Copied to isolate ${input.length} faces",
@@ -425,8 +431,7 @@ class FaceClusteringService {
             dtype: DType.float32,
           ),
           clusterId: face.clusterId,
-          fileCreationTime:
-              fileIDToCreationTime?[getFileIdFromFaceId(face.faceID)],
+          fileCreationTime: fileIDToCreationTime?[getFileIdFromFaceId(face.faceID)],
         ),
       );
     }
@@ -493,9 +498,8 @@ class FaceClusteringService {
       double closestDistance = double.infinity;
       late double thresholdValue;
       if (useDynamicThreshold) {
-        thresholdValue = sortedFaceInfos[i].badFace!
-            ? conservativeDistanceThreshold
-            : distanceThreshold;
+        thresholdValue =
+            sortedFaceInfos[i].badFace! ? conservativeDistanceThreshold : distanceThreshold;
         if (sortedFaceInfos[i].badFace!) dynamicThresholdCount++;
       } else {
         thresholdValue = distanceThreshold;
@@ -505,11 +509,10 @@ class FaceClusteringService {
       }
       // WARNING: The loop below is now O(n^2) so be very careful with anything you put in there!
       for (int j = i - 1; j >= 0; j--) {
-        final double distance = 1 -
-            sortedFaceInfos[i].vEmbedding!.dot(sortedFaceInfos[j].vEmbedding!);
+        final double distance =
+            1 - sortedFaceInfos[i].vEmbedding!.dot(sortedFaceInfos[j].vEmbedding!);
         if (distance < closestDistance) {
-          if (sortedFaceInfos[j].badFace! &&
-              distance > conservativeDistanceThreshold) {
+          if (sortedFaceInfos[j].badFace! && distance > conservativeDistanceThreshold) {
             continue;
           }
           closestDistance = distance;
@@ -535,8 +538,7 @@ class FaceClusteringService {
 
     // Finally, assign the new clusterId to the faces
     final Map<String, int> newFaceIdToCluster = {};
-    final newClusteredFaceInfos =
-        sortedFaceInfos.sublist(alreadyClusteredCount);
+    final newClusteredFaceInfos = sortedFaceInfos.sublist(alreadyClusteredCount);
     for (final faceInfo in newClusteredFaceInfos) {
       newFaceIdToCluster[faceInfo.faceID] = faceInfo.clusterId!;
     }
@@ -597,9 +599,8 @@ class FaceClusteringService {
 
     final Map<int, (Uint8List, int)> newClusterSummaries = {};
     for (final clusterId in newClusterIdToFaceInfos.keys) {
-      final List<Vector> newEmbeddings = newClusterIdToFaceInfos[clusterId]!
-          .map((faceInfo) => faceInfo.vEmbedding!)
-          .toList();
+      final List<Vector> newEmbeddings =
+          newClusterIdToFaceInfos[clusterId]!.map((faceInfo) => faceInfo.vEmbedding!).toList();
       final newCount = newEmbeddings.length;
       if (oldSummary.containsKey(clusterId)) {
         final oldMean = Vector.fromList(
@@ -609,8 +610,7 @@ class FaceClusteringService {
         final oldCount = oldSummary[clusterId]!.$2;
         final oldEmbeddings = oldMean * oldCount;
         newEmbeddings.add(oldEmbeddings);
-        final newMeanVector =
-            newEmbeddings.reduce((a, b) => a + b) / (oldCount + newCount);
+        final newMeanVector = newEmbeddings.reduce((a, b) => a + b) / (oldCount + newCount);
         final newMeanVectorNormalized = newMeanVector / newMeanVector.norm();
         newClusterSummaries[clusterId] = (
           EVector(values: newMeanVectorNormalized.toList()).writeToBuffer(),
@@ -619,10 +619,8 @@ class FaceClusteringService {
       } else {
         final newMeanVector = newEmbeddings.reduce((a, b) => a + b);
         final newMeanVectorNormalized = newMeanVector / newMeanVector.norm();
-        newClusterSummaries[clusterId] = (
-          EVector(values: newMeanVectorNormalized.toList()).writeToBuffer(),
-          newCount
-        );
+        newClusterSummaries[clusterId] =
+            (EVector(values: newMeanVectorNormalized.toList()).writeToBuffer(), newCount);
       }
     }
     log(
@@ -696,6 +694,7 @@ class FaceClusteringService {
     final fileIDToCreationTime = args['fileIDToCreationTime'] as Map<int, int>?;
     final distanceThreshold = args['distanceThreshold'] as double;
     final mergeThreshold = args['mergeThreshold'] as double;
+    final oldClusterSummaries = args['oldClusterSummaries'] as Map<int, (Uint8List, int)>?;
 
     log(
       "[CompleteClustering] ${DateTime.now()} Copied to isolate ${input.length} faces for clustering",
@@ -711,8 +710,7 @@ class FaceClusteringService {
             EVector.fromBuffer(entry.value).values,
             dtype: DType.float32,
           ),
-          fileCreationTime:
-              fileIDToCreationTime?[getFileIdFromFaceId(entry.key)],
+          fileCreationTime: fileIDToCreationTime?[getFileIdFromFaceId(entry.key)],
         ),
       );
     }
@@ -746,8 +744,7 @@ class FaceClusteringService {
       double closestDistance = double.infinity;
       for (int j = 0; j < totalFaces; j++) {
         if (i == j) continue;
-        final double distance =
-            1 - faceInfos[i].vEmbedding!.dot(faceInfos[j].vEmbedding!);
+        final double distance = 1 - faceInfos[i].vEmbedding!.dot(faceInfos[j].vEmbedding!);
         if (distance < closestDistance) {
           closestDistance = distance;
           closestIdx = j;
@@ -777,21 +774,17 @@ class FaceClusteringService {
     }
     final Map<int, (Vector, int)> clusterIdToMeanEmbeddingAndWeight = {};
     for (final clusterId in clusterIdToFaceInfos.keys) {
-      final List<Vector> embeddings = clusterIdToFaceInfos[clusterId]!
-          .map((faceInfo) => faceInfo.vEmbedding!)
-          .toList();
+      final List<Vector> embeddings =
+          clusterIdToFaceInfos[clusterId]!.map((faceInfo) => faceInfo.vEmbedding!).toList();
       final count = clusterIdToFaceInfos[clusterId]!.length;
       final Vector meanEmbedding = embeddings.reduce((a, b) => a + b) / count;
-      final Vector meanEmbeddingNormalized =
-          meanEmbedding / meanEmbedding.norm();
-      clusterIdToMeanEmbeddingAndWeight[clusterId] =
-          (meanEmbeddingNormalized, count);
+      final Vector meanEmbeddingNormalized = meanEmbedding / meanEmbedding.norm();
+      clusterIdToMeanEmbeddingAndWeight[clusterId] = (meanEmbeddingNormalized, count);
     }
 
     // Now merge the clusters that are close to each other, based on mean embedding
     final List<(int, int)> mergedClustersList = [];
-    final List<int> clusterIds =
-        clusterIdToMeanEmbeddingAndWeight.keys.toList();
+    final List<int> clusterIds = clusterIdToMeanEmbeddingAndWeight.keys.toList();
     log(' [CompleteClustering] ${DateTime.now()} ${clusterIds.length} clusters found, now checking for merges');
     while (true) {
       if (clusterIds.length < 2) break;
@@ -858,10 +851,14 @@ class FaceClusteringService {
       }
     }
 
-    final newClusterSummaries = FaceClusteringService.updateClusterSummaries(
-      oldSummary: <int, (Uint8List, int)>{},
-      newFaceInfos: faceInfos,
-    );
+    // Now calculate the mean of the embeddings for each cluster and update the cluster summaries
+    Map<int, (Uint8List, int)>? newClusterSummaries;
+    if (oldClusterSummaries != null) {
+      newClusterSummaries = FaceClusteringService.updateClusterSummaries(
+        oldSummary: oldClusterSummaries,
+        newFaceInfos: faceInfos,
+      );
+    }
 
     stopwatchClustering.stop();
     log(

mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart

@@ -434,7 +434,7 @@ class ClusterFeedbackService {
     );
 
     if (clusterResult == null ||
-        clusterResult.newClusterIdToFaceIds == null ||
+        !clusterResult.hasAllResults ||
         clusterResult.isEmpty) {
       _logger.warning('No clusters found or something went wrong');
       return ClusteringResult(newFaceIdToCluster: {});