[mob][photos] Cluster suggestion calculation in computer
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laurenspriem
parent
2e17e2b78d
commit
793fc3aa46
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@ -1,6 +1,7 @@
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import 'dart:developer' as dev;
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import "dart:math" show Random, min;
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import "package:computer/computer.dart";
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import "package:flutter/foundation.dart";
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import "package:logging/logging.dart";
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import "package:ml_linalg/linalg.dart";
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@ -37,6 +38,7 @@ class ClusterSuggestion {
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class ClusterFeedbackService {
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final Logger _logger = Logger("ClusterFeedbackService");
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final _computer = Computer.shared();
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ClusterFeedbackService._privateConstructor();
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static final ClusterFeedbackService instance =
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@ -257,7 +259,7 @@ class ClusterFeedbackService {
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watch.log('computed avg for ${clusterAvg.length} clusters');
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// Find the actual closest clusters for the person
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final List<(int, double)> suggestions = _calcSuggestionsMean(
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final List<(int, double)> suggestions = await calcSuggestionsMeanInComputer(
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clusterAvg,
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personClusters,
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ignoredClusters,
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@ -491,7 +493,7 @@ class ClusterFeedbackService {
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'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
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);
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final List<(int, double)> suggestionsMeanBigClusters =
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_calcSuggestionsMean(
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await calcSuggestionsMeanInComputer(
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clusterAvgBigClusters,
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personClusters,
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ignoredClusters,
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@ -527,7 +529,8 @@ class ClusterFeedbackService {
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// Find the other cluster candidates based on the median
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final clusterAvg = clusterAvgBigClusters;
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final List<(int, double)> moreSuggestionsMean = _calcSuggestionsMean(
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final List<(int, double)> moreSuggestionsMean =
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await calcSuggestionsMeanInComputer(
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clusterAvg,
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personClusters,
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ignoredClusters,
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@ -775,86 +778,21 @@ class ClusterFeedbackService {
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return clusterAvg;
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}
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/// Returns a map of person's clusterID to map of closest clusterID to with disstance
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List<(int, double)> _calcSuggestionsMean(
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Future<List<(int, double)>> calcSuggestionsMeanInComputer(
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Map<int, Vector> clusterAvg,
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Set<int> personClusters,
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Set<int> ignoredClusters,
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double maxClusterDistance, {
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Map<int, int>? allClusterIdsToCountMap,
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}) {
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final Map<int, List<(int, double)>> suggestions = {};
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const suggestionMax = 2000;
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int suggestionCount = 0;
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int comparisons = 0;
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final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
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// ignore the clusters that belong to the person or is ignored
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Set<int> otherClusters = clusterAvg.keys.toSet().difference(personClusters);
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otherClusters = otherClusters.difference(ignoredClusters);
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for (final otherClusterID in otherClusters) {
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final Vector? otherAvg = clusterAvg[otherClusterID];
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if (otherAvg == null) {
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_logger.warning('no avg for othercluster $otherClusterID');
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continue;
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}
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int? nearestPersonCluster;
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double? minDistance;
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for (final personCluster in personClusters) {
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if (clusterAvg[personCluster] == null) {
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_logger.warning('no avg for personcluster $personCluster');
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continue;
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}
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final Vector avg = clusterAvg[personCluster]!;
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final distance = cosineDistanceSIMD(avg, otherAvg);
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comparisons++;
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if (distance < maxClusterDistance) {
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if (minDistance == null || distance < minDistance) {
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minDistance = distance;
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nearestPersonCluster = personCluster;
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}
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}
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}
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if (nearestPersonCluster != null && minDistance != null) {
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suggestions
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.putIfAbsent(nearestPersonCluster, () => [])
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.add((otherClusterID, minDistance));
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suggestionCount++;
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}
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if (suggestionCount >= suggestionMax) {
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break;
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}
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}
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w?.log(
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'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)',
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double maxClusterDistance,
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) async {
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return await _computer.compute(
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_calcSuggestionsMean,
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param: {
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'clusterAvg': clusterAvg,
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'personClusters': personClusters,
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'ignoredClusters': ignoredClusters,
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'maxClusterDistance': maxClusterDistance,
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},
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);
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if (suggestions.isNotEmpty) {
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final List<(int, double)> suggestClusterIds = [];
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for (final List<(int, double)> suggestion in suggestions.values) {
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suggestClusterIds.addAll(suggestion);
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}
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suggestClusterIds.sort(
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(a, b) => a.$2.compareTo(b.$2),
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); // sort by distance
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// List<int>? suggestClusterIdsSizes;
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// if (allClusterIdsToCountMap != null) {
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// suggestClusterIdsSizes = suggestClusterIds
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// .map((e) => allClusterIdsToCountMap[e.$1]!)
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// .toList(growable: false);
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// }
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// final suggestClusterIdsDistances =
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// suggestClusterIds.map((e) => e.$2).toList(growable: false);
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_logger.info(
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"Already found ${suggestClusterIds.length} good suggestions using mean",
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);
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return suggestClusterIds.sublist(0, min(suggestClusterIds.length, 20));
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} else {
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_logger.info("No suggestions found using mean");
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return <(int, double)>[];
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}
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}
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List<T> _randomSampleWithoutReplacement<T>(
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@ -983,3 +921,77 @@ class ClusterFeedbackService {
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);
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}
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}
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/// Returns a map of person's clusterID to map of closest clusterID to with disstance
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List<(int, double)> _calcSuggestionsMean(Map<String, dynamic> args) {
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// Fill in args
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final Map<int, Vector> clusterAvg = args['clusterAvg'];
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final Set<int> personClusters = args['personClusters'];
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final Set<int> ignoredClusters = args['ignoredClusters'];
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final double maxClusterDistance = args['maxClusterDistance'];
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final Map<int, List<(int, double)>> suggestions = {};
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const suggestionMax = 2000;
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int suggestionCount = 0;
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int comparisons = 0;
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final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
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// ignore the clusters that belong to the person or is ignored
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Set<int> otherClusters = clusterAvg.keys.toSet().difference(personClusters);
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otherClusters = otherClusters.difference(ignoredClusters);
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for (final otherClusterID in otherClusters) {
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final Vector? otherAvg = clusterAvg[otherClusterID];
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if (otherAvg == null) {
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dev.log('[WARNING] no avg for othercluster $otherClusterID');
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continue;
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}
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int? nearestPersonCluster;
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double? minDistance;
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for (final personCluster in personClusters) {
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if (clusterAvg[personCluster] == null) {
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dev.log('[WARNING] no avg for personcluster $personCluster');
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continue;
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}
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final Vector avg = clusterAvg[personCluster]!;
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final distance = cosineDistanceSIMD(avg, otherAvg);
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comparisons++;
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if (distance < maxClusterDistance) {
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if (minDistance == null || distance < minDistance) {
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minDistance = distance;
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nearestPersonCluster = personCluster;
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}
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}
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}
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if (nearestPersonCluster != null && minDistance != null) {
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suggestions
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.putIfAbsent(nearestPersonCluster, () => [])
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.add((otherClusterID, minDistance));
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suggestionCount++;
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}
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if (suggestionCount >= suggestionMax) {
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break;
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}
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}
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w?.log(
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'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)',
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);
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if (suggestions.isNotEmpty) {
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final List<(int, double)> suggestClusterIds = [];
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for (final List<(int, double)> suggestion in suggestions.values) {
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suggestClusterIds.addAll(suggestion);
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}
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suggestClusterIds.sort(
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(a, b) => a.$2.compareTo(b.$2),
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); // sort by distance
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dev.log(
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"Already found ${suggestClusterIds.length} good suggestions using mean",
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);
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return suggestClusterIds.sublist(0, min(suggestClusterIds.length, 20));
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} else {
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dev.log("No suggestions found using mean");
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return <(int, double)>[];
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}
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}
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