Refactor of clustering
This commit is contained in:
laurenspriem
parent
212208ae01
commit
b5cff212bb
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@ -7,6 +7,7 @@ import "dart:typed_data";
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import "package:logging/logging.dart";
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import "package:photos/generated/protos/ente/common/vector.pb.dart";
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import 'package:photos/services/machine_learning/face_ml/face_clustering/cosine_distance.dart';
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import "package:photos/services/machine_learning/face_ml/face_ml_result.dart";
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import "package:synchronized/synchronized.dart";
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class FaceInfo {
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@ -15,10 +16,12 @@ class FaceInfo {
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int? clusterId;
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String? closestFaceId;
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int? closestDist;
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int? fileCreationTime;
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FaceInfo({
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required this.faceID,
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required this.embedding,
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this.clusterId,
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this.fileCreationTime,
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});
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}
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@ -31,7 +34,6 @@ class FaceLinearClustering {
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final Duration _inactivityDuration = const Duration(seconds: 30);
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int _activeTasks = 0;
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final _initLock = Lock();
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late Isolate _isolate;
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@ -220,6 +222,8 @@ class FaceLinearClustering {
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log(
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"[ClusterIsolate] ${DateTime.now()} Copied to isolate ${x.length} faces",
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);
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// Organize everything into a list of FaceInfo objects
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final List<FaceInfo> faceInfos = [];
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for (final entry in x.entries) {
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faceInfos.add(
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@ -249,59 +253,61 @@ class FaceLinearClustering {
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}
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// Sort the faceInfos such that the ones with null clusterId are at the end
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faceInfos.sort((a, b) {
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if (a.clusterId == null && b.clusterId == null) {
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return 0;
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} else if (a.clusterId == null) {
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return 1;
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} else if (b.clusterId == null) {
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return -1;
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} else {
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return 0;
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}
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});
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// Count the amount of null values at the end
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int nullCount = 0;
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for (final faceInfo in faceInfos.reversed) {
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final List<FaceInfo> facesWithClusterID = <FaceInfo>[];
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final List<FaceInfo> facesWithoutClusterID = <FaceInfo>[];
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for (final FaceInfo faceInfo in faceInfos) {
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if (faceInfo.clusterId == null) {
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nullCount++;
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facesWithoutClusterID.add(faceInfo);
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} else {
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break;
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facesWithClusterID.add(faceInfo);
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}
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}
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final sortedFaceInfos = <FaceInfo>[];
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sortedFaceInfos.addAll(facesWithClusterID);
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sortedFaceInfos.addAll(facesWithoutClusterID);
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log(
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"[ClusterIsolate] ${DateTime.now()} Clustering $nullCount new faces without clusterId, and ${faceInfos.length - nullCount} faces with clusterId",
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"[ClusterIsolate] ${DateTime.now()} Clustering ${facesWithoutClusterID.length} new faces without clusterId, and ${facesWithClusterID.length} faces with clusterId",
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);
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for (final clusteredFaceInfo
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in faceInfos.sublist(0, faceInfos.length - nullCount)) {
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assert(clusteredFaceInfo.clusterId != null);
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// Make sure the first face has a clusterId
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final int totalFaces = sortedFaceInfos.length;
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int clusterID = 1;
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if (sortedFaceInfos.isNotEmpty) {
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if (sortedFaceInfos.first.clusterId == null) {
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sortedFaceInfos.first.clusterId = clusterID;
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} else {
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clusterID = sortedFaceInfos.first.clusterId!;
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}
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} else {
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return {};
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}
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final int totalFaces = faceInfos.length;
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int clusterID = 1;
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if (faceInfos.isNotEmpty) {
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faceInfos.first.clusterId = clusterID;
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}
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// Start actual clustering
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log(
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"[ClusterIsolate] ${DateTime.now()} Processing $totalFaces faces",
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);
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final Map<String, int> newFaceIdToCluster = {};
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final stopwatchClustering = Stopwatch()..start();
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for (int i = 1; i < totalFaces; i++) {
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// Incremental clustering, so we can skip faces that already have a clusterId
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if (faceInfos[i].clusterId != null) {
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clusterID = max(clusterID, faceInfos[i].clusterId!);
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if (sortedFaceInfos[i].clusterId != null) {
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clusterID = max(clusterID, sortedFaceInfos[i].clusterId!);
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if (i % 250 == 0) {
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log("[ClusterIsolate] ${DateTime.now()} First $i faces already had a clusterID");
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}
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continue;
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}
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final currentEmbedding = faceInfos[i].embedding;
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final currentEmbedding = sortedFaceInfos[i].embedding;
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int closestIdx = -1;
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double closestDistance = double.infinity;
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if (i % 250 == 0) {
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log("[ClusterIsolate] ${DateTime.now()} Processing $i faces");
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}
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for (int j = 0; j < i; j++) {
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for (int j = i - 1; j >= 0; j--) {
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final double distance = cosineDistForNormVectors(
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currentEmbedding,
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faceInfos[j].embedding,
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sortedFaceInfos[j].embedding,
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);
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if (distance < closestDistance) {
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closestDistance = distance;
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@ -310,42 +316,43 @@ class FaceLinearClustering {
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}
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if (closestDistance < recommendedDistanceThreshold) {
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if (faceInfos[closestIdx].clusterId == null) {
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if (sortedFaceInfos[closestIdx].clusterId == null) {
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// Ideally this should never happen, but just in case log it
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log(
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" [ClusterIsolate] ${DateTime.now()} Found new cluster $clusterID",
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" [ClusterIsolate] [WARNING] ${DateTime.now()} Found new cluster $clusterID",
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);
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clusterID++;
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faceInfos[closestIdx].clusterId = clusterID;
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sortedFaceInfos[closestIdx].clusterId = clusterID;
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newFaceIdToCluster[sortedFaceInfos[closestIdx].faceID] = clusterID;
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}
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faceInfos[i].clusterId = faceInfos[closestIdx].clusterId;
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sortedFaceInfos[i].clusterId = sortedFaceInfos[closestIdx].clusterId;
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newFaceIdToCluster[sortedFaceInfos[i].faceID] =
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sortedFaceInfos[closestIdx].clusterId!;
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} else {
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clusterID++;
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faceInfos[i].clusterId = clusterID;
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sortedFaceInfos[i].clusterId = clusterID;
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newFaceIdToCluster[sortedFaceInfos[i].faceID] = clusterID;
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}
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}
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final Map<String, int> result = {};
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for (final faceInfo in faceInfos) {
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result[faceInfo.faceID] = faceInfo.clusterId!;
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}
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stopwatchClustering.stop();
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log(
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' [ClusterIsolate] ${DateTime.now()} Clustering for ${faceInfos.length} embeddings (${faceInfos[0].embedding.length} size) executed in ${stopwatchClustering.elapsedMilliseconds}ms, clusters $clusterID',
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' [ClusterIsolate] ${DateTime.now()} Clustering for ${sortedFaceInfos.length} embeddings (${sortedFaceInfos[0].embedding.length} size) executed in ${stopwatchClustering.elapsedMilliseconds}ms, clusters $clusterID',
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);
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// return result;
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// NOTe: The main clustering logic is done, the following is just filtering and logging
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final input = x;
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final faceIdToCluster = result;
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stopwatchClustering.reset();
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stopwatchClustering.start();
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// analyze the results
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FaceLinearClustering._analyzeClusterResults(sortedFaceInfos);
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final Set<String> newFaceIds = <String>{};
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input.forEach((key, value) {
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if (value.$1 == null) {
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newFaceIds.add(key);
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return newFaceIdToCluster;
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}
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static void _analyzeClusterResults(List<FaceInfo> sortedFaceInfos) {
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final stopwatch = Stopwatch()..start();
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final Map<String, int> faceIdToCluster = {};
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for (final faceInfo in sortedFaceInfos) {
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faceIdToCluster[faceInfo.faceID] = faceInfo.clusterId!;
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}
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});
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// Find faceIDs that are part of a cluster which is larger than 5 and are new faceIDs
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final Map<int, int> clusterIdToSize = {};
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@ -356,12 +363,6 @@ class FaceLinearClustering {
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clusterIdToSize[value] = 1;
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}
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});
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final Map<String, int> faceIdToClusterFiltered = {};
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for (final entry in faceIdToCluster.entries) {
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if (clusterIdToSize[entry.value]! > 0 && newFaceIds.contains(entry.key)) {
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faceIdToClusterFiltered[entry.key] = entry.value;
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}
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}
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// print top 10 cluster ids and their sizes based on the internal cluster id
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final clusterIds = faceIdToCluster.values.toSet();
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@ -369,7 +370,7 @@ class FaceLinearClustering {
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return faceIdToCluster.values.where((id) => id == clusterId).length;
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}).toList();
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clusterSizes.sort();
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// find clusters whose size is graeter than 1
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// find clusters whose size is greater than 1
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int oneClusterCount = 0;
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int moreThan5Count = 0;
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int moreThan10Count = 0;
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@ -377,57 +378,29 @@ class FaceLinearClustering {
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int moreThan50Count = 0;
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int moreThan100Count = 0;
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// for (int i = 0; i < clusterSizes.length; i++) {
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// if (clusterSizes[i] > 100) {
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// moreThan100Count++;
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// } else if (clusterSizes[i] > 50) {
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// moreThan50Count++;
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// } else if (clusterSizes[i] > 20) {
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// moreThan20Count++;
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// } else if (clusterSizes[i] > 10) {
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// moreThan10Count++;
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// } else if (clusterSizes[i] > 5) {
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// moreThan5Count++;
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// } else if (clusterSizes[i] == 1) {
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// oneClusterCount++;
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// }
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// }
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for (int i = 0; i < clusterSizes.length; i++) {
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if (clusterSizes[i] > 100) {
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moreThan100Count++;
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}
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if (clusterSizes[i] > 50) {
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} else if (clusterSizes[i] > 50) {
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moreThan50Count++;
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}
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if (clusterSizes[i] > 20) {
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} else if (clusterSizes[i] > 20) {
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moreThan20Count++;
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}
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if (clusterSizes[i] > 10) {
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} else if (clusterSizes[i] > 10) {
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moreThan10Count++;
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}
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if (clusterSizes[i] > 5) {
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} else if (clusterSizes[i] > 5) {
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moreThan5Count++;
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}
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if (clusterSizes[i] == 1) {
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} else if (clusterSizes[i] == 1) {
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oneClusterCount++;
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}
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}
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// print the metrics
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log(
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'[ClusterIsolate] Total clusters ${clusterIds.length}, '
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'oneClusterCount $oneClusterCount, '
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'moreThan5Count $moreThan5Count, '
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'moreThan10Count $moreThan10Count, '
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'moreThan20Count $moreThan20Count, '
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'moreThan50Count $moreThan50Count, '
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'moreThan100Count $moreThan100Count',
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"[ClusterIsolate] Total clusters ${clusterIds.length}: \n oneClusterCount $oneClusterCount \n moreThan5Count $moreThan5Count \n moreThan10Count $moreThan10Count \n moreThan20Count $moreThan20Count \n moreThan50Count $moreThan50Count \n moreThan100Count $moreThan100Count",
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);
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stopwatchClustering.stop();
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stopwatch.stop();
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log(
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"[ClusterIsolate] Clustering additional steps took ${stopwatchClustering.elapsedMilliseconds} ms",
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"[ClusterIsolate] Clustering additional analysis took ${stopwatch.elapsedMilliseconds} ms",
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);
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// log('Top clusters count ${clusterSizes.reversed.take(10).toList()}');
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return faceIdToClusterFiltered;
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}
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}
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