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[mob][photos] Remove redundant logging

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laurenspriem 2024-04-24 16:31:36 +05:30
parent 3806ee3232
commit 05a4e9f90b
1 changed files with 16 additions and 23 deletions
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39
mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
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@ -456,20 +456,17 @@ class ClusterFeedbackService {
double maxMeanDistance = 0.65,
double goodMeanDistance = 0.54,
}) async {
final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
// Get all the cluster data
final startTime = DateTime.now();
final faceMlDb = FaceMLDataDB.instance;
// final Map<int, List<(int, double)>> suggestions = {};
final allClusterIdsToCountMap = await faceMlDb.clusterIdToFaceCount();
final ignoredClusters = await faceMlDb.getPersonIgnoredClusters(p.remoteID);
final personClusters = await faceMlDb.getPersonClusterIDs(p.remoteID);
dev.log(
'${p.data.name} has ${personClusters.length} existing clusters, getting all database data took ${DateTime.now().difference(startTime).inMilliseconds} ms',
name: "getSuggestionsUsingMedian",
w?.log(
'${p.data.name} has ${personClusters.length} existing clusters, getting all database data done',
);
// First only do a simple check on the big clusters, if the person does not have small clusters yet
final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
final smallestPersonClusterSize = personClusters
.map((clusterID) => allClusterIdsToCountMap[clusterID] ?? 0)
.reduce((value, element) => min(value, element));
@ -482,10 +479,9 @@ class ClusterFeedbackService {
ignoredClusters,
minClusterSize: minimumSize,
);
dev.log(
'computed avg for ${clusterAvgBigClusters.length} clusters, in ${DateTime.now().difference(startTime).inMilliseconds} ms',
w?.log(
'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
);
w?.log('Calculate avg for min size $minimumSize');
final List<(int, double)> suggestionsMeanBigClusters =
_calcSuggestionsMean(
clusterAvgBigClusters,
@ -493,7 +489,9 @@ class ClusterFeedbackService {
ignoredClusters,
goodMeanDistance,
);
w?.log('Calculate suggestions using mean for min size $minimumSize');
w?.log(
'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
);
if (suggestionsMeanBigClusters.isNotEmpty) {
return suggestionsMeanBigClusters
.map((e) => (e.$1, e.$2, true))
@ -508,8 +506,8 @@ class ClusterFeedbackService {
allClusterIdsToCountMap,
ignoredClusters,
);
dev.log(
'computed avg for ${clusterAvg.length} clusters, in ${DateTime.now().difference(startTime).inMilliseconds} ms',
w?.log(
'computed avg for ${clusterAvg.length} clusters,',
);
// Find the other cluster candidates based on the mean
@ -649,6 +647,7 @@ class ClusterFeedbackService {
int maxClusterInCurrentRun = 500,
int maxEmbeddingToRead = 10000,
}) async {
final w = (kDebugMode ? EnteWatch('_getUpdateClusterAvg') : null)?..start();
final startTime = DateTime.now();
final faceMlDb = FaceMLDataDB.instance;
_logger.info(
@ -661,8 +660,8 @@ class ClusterFeedbackService {
final Map<int, Vector> clusterAvg = {};
dev.log(
'getUpdateClusterAvg database call for getAllClusterSummary took ${DateTime.now().difference(startTime).inMilliseconds} ms',
w?.log(
'getUpdateClusterAvg database call for getAllClusterSummary',
);
final allClusterIds = allClusterIdsToCountMap.keys.toSet();
@ -687,8 +686,8 @@ class ClusterFeedbackService {
smallerClustersCnt++;
}
}
dev.log(
'serialization of embeddings took ${DateTime.now().difference(serializationTime).inMilliseconds} ms',
w?.log(
'serialization of embeddings',
);
_logger.info(
'Ignored $ignoredClustersCnt clusters, already updated $alreadyUpdatedClustersCnt clusters, $smallerClustersCnt clusters are smaller than $minClusterSize',
@ -708,12 +707,7 @@ class ClusterFeedbackService {
allClusterIdsToCountMap[b]!.compareTo(allClusterIdsToCountMap[a]!),
);
int indexedInCurrentRun = 0;
final EnteWatch? w = kDebugMode ? EnteWatch("computeAvg") : null;
w?.start();
w?.log(
'reading embeddings for $maxClusterInCurrentRun or ${sortedClusterIDs.length} clusters',
);
w?.reset();
int currentPendingRead = 0;
final List<int> clusterIdsToRead = [];
@ -790,7 +784,6 @@ class ClusterFeedbackService {
final Map<int, List<(int, double)>> suggestions = {};
int suggestionCount = 0;
final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
w?.log('converted avg to vectors for ${clusterAvg.length} averages');
for (final otherClusterID in clusterAvg.keys) {
// ignore the cluster that belong to the person or is ignored
if (personClusters.contains(otherClusterID) ||