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[mob] compute suggestion in small batches

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This commit is contained in:
Neeraj Gupta 2024-04-02 16:56:55 +05:30
parent e2ed836b16
commit faa07a0704
1 changed files with 53 additions and 31 deletions
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84
mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
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@ -367,11 +367,13 @@ class ClusterFeedbackService {
Future<Map<int, List<double>>> _getUpdateClusterAvg(
Map<int, int> allClusterIdsToCountMap,
Set<int> ignoredClusters,
) async {
Set<int> ignoredClusters, {
int minClusterSize = 1,
int maxClusterInCurrentRun = 500,
}) async {
final faceMlDb = FaceMLDataDB.instance;
_logger.info(
'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters',
'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters, minClusterSize $minClusterSize, maxClusterInCurrentRun $maxClusterInCurrentRun',
);
final Map<int, (Uint8List, int)> clusterToSummary =
@ -380,42 +382,61 @@ class ClusterFeedbackService {
final Map<int, List<double>> clusterAvg = {};
final allClusterIds = allClusterIdsToCountMap.keys;
for (final clusterID in allClusterIds) {
if (ignoredClusters.contains(clusterID)) {
continue;
final allClusterIds = allClusterIdsToCountMap.keys.toSet();
int ignoredClustersCnt = 0, alreadyUpdatedClustersCnt = 0;
int smallerClustersCnt = 0;
for (final id in allClusterIdsToCountMap.keys) {
if (ignoredClusters.contains(id)) {
allClusterIds.remove(id);
ignoredClustersCnt++;
}
if (allClusterIdsToCountMap[clusterID]! < 2) {
continue;
if (clusterToSummary[id]?.$2 == allClusterIdsToCountMap[id]) {
allClusterIds.remove(id);
clusterAvg[id] = EVector.fromBuffer(clusterToSummary[id]!.$1).values;
alreadyUpdatedClustersCnt++;
}
if (allClusterIdsToCountMap[id]! < minClusterSize) {
allClusterIds.remove(id);
smallerClustersCnt++;
}
}
_logger.info(
'Ignored $ignoredClustersCnt clusters, already updated $alreadyUpdatedClustersCnt clusters, $smallerClustersCnt clusters are smaller than $minClusterSize',
);
// get clusterIDs sorted by count in descending order
final sortedClusterIDs = allClusterIds.toList();
sortedClusterIDs.sort(
(a, b) =>
allClusterIdsToCountMap[b]!.compareTo(allClusterIdsToCountMap[a]!),
);
int indexedInCurrentRun = 0;
late List<double> avg;
if (clusterToSummary[clusterID]?.$2 ==
allClusterIdsToCountMap[clusterID]) {
avg = EVector.fromBuffer(clusterToSummary[clusterID]!.$1).values;
} else {
final Iterable<Uint8List> embedings =
await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
final List<double> sum = List.filled(192, 0);
for (final embedding in embedings) {
final data = EVector.fromBuffer(embedding).values;
for (int i = 0; i < sum.length; i++) {
sum[i] += data[i];
}
}
avg = sum.map((e) => e / embedings.length).toList();
final avgEmbeedingBuffer = EVector(values: avg).writeToBuffer();
updatesForClusterSummary[clusterID] =
(avgEmbeedingBuffer, embedings.length);
for (final clusterID in sortedClusterIDs) {
if (maxClusterInCurrentRun-- <= 0) {
break;
}
indexedInCurrentRun++;
late List<double> avg;
final Iterable<Uint8List> embedings =
await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
final List<double> sum = List.filled(192, 0);
for (final embedding in embedings) {
final data = EVector.fromBuffer(embedding).values;
for (int i = 0; i < sum.length; i++) {
sum[i] += data[i];
}
}
avg = sum.map((e) => e / embedings.length).toList();
final avgEmbeedingBuffer = EVector(values: avg).writeToBuffer();
updatesForClusterSummary[clusterID] =
(avgEmbeedingBuffer, embedings.length);
// store the intermediate updates
if (updatesForClusterSummary.length > 100) {
await faceMlDb.clusterSummaryUpdate(updatesForClusterSummary);
updatesForClusterSummary.clear();
if (kDebugMode) {
_logger.info(
'start getUpdateClusterAvg for ${allClusterIdsToCountMap.length} clusters',
);
'getUpdateClusterAvg $indexedInCurrentRun clusters in current one');
}
}
clusterAvg[clusterID] = avg;
@ -549,8 +570,9 @@ class ClusterFeedbackService {
);
}
suggestion.$4.sort((b, a) {
final double distanceA = fileIdToDistanceMap[a.uploadedFileID!];
final double distanceB = fileIdToDistanceMap[b.uploadedFileID!];
//todo: review with @laurens, added this to avoid null safety issue
final double distanceA = fileIdToDistanceMap[a.uploadedFileID!] ?? -1;
final double distanceB = fileIdToDistanceMap[b.uploadedFileID!] ?? -1;
return distanceA.compareTo(distanceB);
});