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[mob][photos] Cleanup feedback
This commit is contained in:
laurenspriem
parent
7c535adff1
commit
aab7e39ab3
@ -1080,16 +1080,4 @@ class FaceMLDataDB {
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}
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await forceUpdateClusterIds(faceIDToClusterID);
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}
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Future<void> addFacesToCluster(
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List<String> faceIDs,
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int clusterID,
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) async {
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final faceIDToClusterID = <String, int>{};
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for (final faceID in faceIDs) {
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faceIDToClusterID[faceID] = clusterID;
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}
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await forceUpdateClusterIds(faceIDToClusterID);
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}
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}
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@ -244,8 +244,12 @@ class ClusterFeedbackService {
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}
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}
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Future<void> addFilesToCluster(List<String> faceIDs, int clusterID) async {
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await FaceMLDataDB.instance.addFacesToCluster(faceIDs, clusterID);
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Future<void> addFacesToCluster(List<String> faceIDs, int clusterID) async {
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final faceIDToClusterID = <String, int>{};
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for (final faceID in faceIDs) {
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faceIDToClusterID[faceID] = clusterID;
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}
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await FaceMLDataDB.instance.forceUpdateClusterIds(faceIDToClusterID);
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Bus.instance.fire(PeopleChangedEvent());
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return;
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}
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@ -411,7 +415,6 @@ class ClusterFeedbackService {
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return susClusters;
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}
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// TODO: iterate over this method to find sweet spot
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Future<ClusteringResult> breakUpCluster(
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int clusterID, {
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bool useDbscan = false,
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@ -468,178 +471,6 @@ class ClusterFeedbackService {
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return clusterResult;
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}
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/// WARNING: this method is purely for debugging purposes, never use in production
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Future<void> createFakeClustersByBlurValue() async {
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try {
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// Delete old clusters
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await FaceMLDataDB.instance.dropClustersAndPersonTable();
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final List<PersonEntity> persons =
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await PersonService.instance.getPersons();
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for (final PersonEntity p in persons) {
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await PersonService.instance.deletePerson(p.remoteID);
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}
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// Create new fake clusters based on blur value. One for values between 0 and 10, one for 10-20, etc till 200
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final int startClusterID = DateTime.now().microsecondsSinceEpoch;
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final faceIDsToBlurValues =
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await FaceMLDataDB.instance.getFaceIDsToBlurValues(200);
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final faceIdToCluster = <String, int>{};
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for (final entry in faceIDsToBlurValues.entries) {
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final faceID = entry.key;
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final blurValue = entry.value;
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final newClusterID = startClusterID + blurValue ~/ 10;
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faceIdToCluster[faceID] = newClusterID;
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}
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await FaceMLDataDB.instance.updateFaceIdToClusterId(faceIdToCluster);
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Bus.instance.fire(PeopleChangedEvent());
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} catch (e, s) {
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_logger.severe("Error in createFakeClustersByBlurValue", e, s);
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rethrow;
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}
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}
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Future<void> debugLogClusterBlurValues(
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int clusterID, {
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int? clusterSize,
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bool logClusterSummary = false,
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bool logBlurValues = false,
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}) async {
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if (!kDebugMode) return;
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// Logging the clusterID
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_logger.info(
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"Debug logging for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}",
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);
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const int biggestClusterID = 1715061228725148;
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// Logging the cluster summary for the cluster
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if (logClusterSummary) {
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final summaryMap = await FaceMLDataDB.instance.getClusterToClusterSummary(
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[clusterID, biggestClusterID],
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);
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final summary = summaryMap[clusterID];
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if (summary != null) {
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_logger.info(
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"Cluster summary for cluster $clusterID says the amount of faces is: ${summary.$2}",
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);
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}
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final biggestClusterSummary = summaryMap[biggestClusterID];
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final clusterSummary = summaryMap[clusterID];
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if (biggestClusterSummary != null && clusterSummary != null) {
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_logger.info(
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"Cluster summary for biggest cluster $biggestClusterID says the size is: ${biggestClusterSummary.$2}",
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);
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_logger.info(
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"Cluster summary for current cluster $clusterID says the size is: ${clusterSummary.$2}",
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);
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// Mean distance
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final biggestMean = Vector.fromList(
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EVector.fromBuffer(biggestClusterSummary.$1).values,
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dtype: DType.float32,
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);
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final currentMean = Vector.fromList(
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EVector.fromBuffer(clusterSummary.$1).values,
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dtype: DType.float32,
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);
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final bigClustersMeanDistance = 1 - biggestMean.dot(currentMean);
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_logger.info(
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"Mean distance between biggest cluster and current cluster: $bigClustersMeanDistance",
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);
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_logger.info(
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'Element differences between the two means are ${biggestMean - currentMean}',
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);
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final currentL2Norm = currentMean.norm();
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_logger.info(
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'L2 norm of current mean: $currentL2Norm',
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);
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final trueDistance =
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biggestMean.distanceTo(currentMean, distance: Distance.cosine);
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_logger.info('True distance between the two means: $trueDistance');
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// Median distance
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const sampleSize = 100;
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final Iterable<Uint8List> biggestEmbeddings = await FaceMLDataDB
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.instance
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.getFaceEmbeddingsForCluster(biggestClusterID);
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final List<Uint8List> biggestSampledEmbeddingsProto =
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_randomSampleWithoutReplacement(
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biggestEmbeddings,
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sampleSize,
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);
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final List<Vector> biggestSampledEmbeddings =
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biggestSampledEmbeddingsProto
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.map(
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(embedding) => Vector.fromList(
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EVector.fromBuffer(embedding).values,
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dtype: DType.float32,
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),
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)
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.toList(growable: false);
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final Iterable<Uint8List> currentEmbeddings =
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await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
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final List<Uint8List> currentSampledEmbeddingsProto =
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_randomSampleWithoutReplacement(
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currentEmbeddings,
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sampleSize,
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);
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final List<Vector> currentSampledEmbeddings =
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currentSampledEmbeddingsProto
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.map(
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(embedding) => Vector.fromList(
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EVector.fromBuffer(embedding).values,
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dtype: DType.float32,
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),
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)
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.toList(growable: false);
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// Calculate distances and find the median
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final List<double> distances = [];
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final List<double> trueDistances = [];
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for (final biggestEmbedding in biggestSampledEmbeddings) {
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for (final currentEmbedding in currentSampledEmbeddings) {
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distances.add(1 - biggestEmbedding.dot(currentEmbedding));
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trueDistances.add(
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biggestEmbedding.distanceTo(
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currentEmbedding,
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distance: Distance.cosine,
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),
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);
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}
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}
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distances.sort();
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trueDistances.sort();
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final double medianDistance = distances[distances.length ~/ 2];
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final double trueMedianDistance =
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trueDistances[trueDistances.length ~/ 2];
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_logger.info(
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"Median distance between biggest cluster and current cluster: $medianDistance (using sample of $sampleSize)",
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);
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_logger.info(
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'True distance median between the two embeddings: $trueMedianDistance',
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);
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}
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}
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// Logging the blur values for the cluster
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if (logBlurValues) {
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final List<double> blurValues = await FaceMLDataDB.instance
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.getBlurValuesForCluster(clusterID)
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.then((value) => value.toList());
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final blurValuesIntegers =
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blurValues.map((value) => value.round()).toList();
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blurValuesIntegers.sort();
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_logger.info(
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"Blur values for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}: $blurValuesIntegers",
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);
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}
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return;
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}
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/// Returns a list of suggestions. For each suggestion we return a record consisting of the following elements:
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/// 1. clusterID: the ID of the cluster
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/// 2. distance: the distance between the person's cluster and the suggestion
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@ -1126,6 +957,147 @@ class ClusterFeedbackService {
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"Sorting suggestions based on distance to person took ${endTime.difference(startTime).inMilliseconds} ms for ${suggestions.length} suggestions, of which ${clusterSummaryCallTime.difference(startTime).inMilliseconds} ms was spent on the cluster summary call",
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);
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}
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Future<void> debugLogClusterBlurValues(
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int clusterID, {
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int? clusterSize,
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bool logClusterSummary = false,
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bool logBlurValues = false,
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}) async {
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if (!kDebugMode) return;
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// Logging the clusterID
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_logger.info(
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"Debug logging for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}",
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);
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const int biggestClusterID = 1715061228725148;
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// Logging the cluster summary for the cluster
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if (logClusterSummary) {
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final summaryMap = await FaceMLDataDB.instance.getClusterToClusterSummary(
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[clusterID, biggestClusterID],
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);
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final summary = summaryMap[clusterID];
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if (summary != null) {
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_logger.info(
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"Cluster summary for cluster $clusterID says the amount of faces is: ${summary.$2}",
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);
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}
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final biggestClusterSummary = summaryMap[biggestClusterID];
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final clusterSummary = summaryMap[clusterID];
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if (biggestClusterSummary != null && clusterSummary != null) {
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_logger.info(
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"Cluster summary for biggest cluster $biggestClusterID says the size is: ${biggestClusterSummary.$2}",
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);
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_logger.info(
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"Cluster summary for current cluster $clusterID says the size is: ${clusterSummary.$2}",
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);
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// Mean distance
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final biggestMean = Vector.fromList(
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EVector.fromBuffer(biggestClusterSummary.$1).values,
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dtype: DType.float32,
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);
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final currentMean = Vector.fromList(
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EVector.fromBuffer(clusterSummary.$1).values,
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dtype: DType.float32,
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);
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final bigClustersMeanDistance = 1 - biggestMean.dot(currentMean);
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_logger.info(
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"Mean distance between biggest cluster and current cluster: $bigClustersMeanDistance",
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);
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_logger.info(
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'Element differences between the two means are ${biggestMean - currentMean}',
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);
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final currentL2Norm = currentMean.norm();
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_logger.info(
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'L2 norm of current mean: $currentL2Norm',
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);
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final trueDistance =
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biggestMean.distanceTo(currentMean, distance: Distance.cosine);
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_logger.info('True distance between the two means: $trueDistance');
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// Median distance
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const sampleSize = 100;
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final Iterable<Uint8List> biggestEmbeddings = await FaceMLDataDB
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.instance
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.getFaceEmbeddingsForCluster(biggestClusterID);
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final List<Uint8List> biggestSampledEmbeddingsProto =
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_randomSampleWithoutReplacement(
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biggestEmbeddings,
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sampleSize,
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);
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final List<Vector> biggestSampledEmbeddings =
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biggestSampledEmbeddingsProto
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.map(
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(embedding) => Vector.fromList(
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EVector.fromBuffer(embedding).values,
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dtype: DType.float32,
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),
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)
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.toList(growable: false);
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final Iterable<Uint8List> currentEmbeddings =
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await FaceMLDataDB.instance.getFaceEmbeddingsForCluster(clusterID);
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final List<Uint8List> currentSampledEmbeddingsProto =
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_randomSampleWithoutReplacement(
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currentEmbeddings,
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sampleSize,
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);
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final List<Vector> currentSampledEmbeddings =
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currentSampledEmbeddingsProto
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.map(
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(embedding) => Vector.fromList(
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EVector.fromBuffer(embedding).values,
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dtype: DType.float32,
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),
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)
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.toList(growable: false);
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// Calculate distances and find the median
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final List<double> distances = [];
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final List<double> trueDistances = [];
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for (final biggestEmbedding in biggestSampledEmbeddings) {
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for (final currentEmbedding in currentSampledEmbeddings) {
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distances.add(1 - biggestEmbedding.dot(currentEmbedding));
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trueDistances.add(
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biggestEmbedding.distanceTo(
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currentEmbedding,
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distance: Distance.cosine,
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),
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);
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}
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}
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distances.sort();
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trueDistances.sort();
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final double medianDistance = distances[distances.length ~/ 2];
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final double trueMedianDistance =
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trueDistances[trueDistances.length ~/ 2];
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_logger.info(
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"Median distance between biggest cluster and current cluster: $medianDistance (using sample of $sampleSize)",
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);
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_logger.info(
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'True distance median between the two embeddings: $trueMedianDistance',
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);
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}
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}
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// Logging the blur values for the cluster
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if (logBlurValues) {
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final List<double> blurValues = await FaceMLDataDB.instance
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.getBlurValuesForCluster(clusterID)
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.then((value) => value.toList());
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final blurValuesIntegers =
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blurValues.map((value) => value.round()).toList();
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blurValuesIntegers.sort();
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_logger.info(
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"Blur values for cluster $clusterID${clusterSize != null ? ' with $clusterSize photos' : ''}: $blurValuesIntegers",
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);
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}
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return;
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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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@ -279,7 +279,7 @@ class _FaceWidgetState extends State<FaceWidget> {
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try {
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if (isJustRemoved) {
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await ClusterFeedbackService.instance
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.addFilesToCluster([widget.face.faceID], widget.clusterID!);
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.addFacesToCluster([widget.face.faceID], widget.clusterID!);
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} else {
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await ClusterFeedbackService.instance
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.removeFilesFromCluster([widget.file], widget.clusterID!);
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