[mob] Use vector for cosine dist

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

@@ -5,6 +5,8 @@ import "dart:math" show max;
 import "dart:typed_data";
 
 import "package:logging/logging.dart";
+import "package:ml_linalg/dtype.dart";
+import "package:ml_linalg/vector.dart";
 import "package:photos/generated/protos/ente/common/vector.pb.dart";
 import 'package:photos/services/machine_learning/face_ml/face_clustering/cosine_distance.dart';
 import "package:photos/services/machine_learning/face_ml/face_ml_result.dart";
@@ -12,14 +14,16 @@ import "package:synchronized/synchronized.dart";
 
 class FaceInfo {
   final String faceID;
-  final List<double> embedding;
+  final List<double>? embedding;
+  final Vector? vEmbedding;
   int? clusterId;
   String? closestFaceId;
   int? closestDist;
   int? fileCreationTime;
   FaceInfo({
     required this.faceID,
-    required this.embedding,
+    this.embedding,
+    this.vEmbedding,
     this.clusterId,
     this.fileCreationTime,
   });
@@ -230,7 +234,10 @@ class FaceLinearClustering {
       faceInfos.add(
         FaceInfo(
           faceID: entry.key,
-          embedding: EVector.fromBuffer(entry.value.$2).values,
+          vEmbedding: Vector.fromList(
+            EVector.fromBuffer(entry.value.$2).values,
+            dtype: DType.float32,
+          ),
           clusterId: entry.value.$1,
           fileCreationTime:
               fileIDToCreationTime?[getFileIdFromFaceId(entry.key)],
@@ -300,17 +307,25 @@ class FaceLinearClustering {
         }
         continue;
       }
-      final currentEmbedding = sortedFaceInfos[i].embedding;
+
       int closestIdx = -1;
       double closestDistance = double.infinity;
       if (i % 250 == 0) {
         log("[ClusterIsolate] ${DateTime.now()} Processing $i faces");
       }
       for (int j = i - 1; j >= 0; j--) {
-        final double distance = cosineDistForNormVectors(
-          currentEmbedding,
-          sortedFaceInfos[j].embedding,
-        );
+        late double distance;
+        if (sortedFaceInfos[i].vEmbedding != null) {
+          distance = 1.0 -
+              sortedFaceInfos[i]
+                  .vEmbedding!
+                  .dot(sortedFaceInfos[j].vEmbedding!);
+        } else {
+          distance = cosineDistForNormVectors(
+            sortedFaceInfos[i].embedding!,
+            sortedFaceInfos[j].embedding!,
+          );
+        }
         if (distance < closestDistance) {
           closestDistance = distance;
           closestIdx = j;
@@ -339,7 +354,7 @@ class FaceLinearClustering {
 
     stopwatchClustering.stop();
     log(
-      ' [ClusterIsolate] ${DateTime.now()} Clustering for ${sortedFaceInfos.length} embeddings (${sortedFaceInfos[0].embedding.length} size) executed in ${stopwatchClustering.elapsedMilliseconds}ms, clusters $clusterID',
+      ' [ClusterIsolate] ${DateTime.now()} Clustering for ${sortedFaceInfos.length} embeddings executed in ${stopwatchClustering.elapsedMilliseconds}ms, clusters $clusterID',
     );
 
     // analyze the results

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

@@ -389,6 +389,10 @@ class ClusterFeedbackService {
       if (ignoredClusters.contains(clusterID)) {
         continue;
       }
+      if (allClusterIdsToCountMap[clusterID]! < 2) {
+        continue;
+      }
+
       late List<double> avg;
       if (clusterToSummary[clusterID]?.$2 ==
           allClusterIdsToCountMap[clusterID]) {
@@ -412,6 +416,11 @@ class ClusterFeedbackService {
       if (updatesForClusterSummary.length > 100) {
         await faceMlDb.clusterSummaryUpdate(updatesForClusterSummary);
         updatesForClusterSummary.clear();
+        if (kDebugMode) {
+          _logger.info(
+            'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters',
+          );
+        }
       }
       clusterAvg[clusterID] = avg;
     }
@@ -441,6 +450,10 @@ class ClusterFeedbackService {
       int? nearestPersonCluster;
       double? minDistance;
       for (final personCluster in personClusters) {
+        if (clusterAvg[personCluster] == null) {
+          _logger.info('no avg for cluster $personCluster');
+          continue;
+        }
         final avg = clusterAvg[personCluster]!;
         final distance = cosineDistForNormVectors(avg, otherAvg);
         if (distance < maxClusterDistance) {

mobile/lib/ui/viewer/search/result/person_face_widget.dart

@@ -93,7 +93,7 @@ class PersonFaceWidget extends StatelessWidget {
       );
       if (face == null) {
         debugPrint(
-          "No cover face for person: $personId and cluster $clusterID",
+          "No cover face for person: $personId and cluster $clusterID and recentFile ${file.uploadedFileID}",
         );
         return null;
       }