TerribleDev/ente
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

More params

Browse Source
This commit is contained in:
Manav Rathi
2024-09-02 16:47:49 +05:30
parent 30d5fedb11
commit 371dcf8ab9
2 changed files with 47 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
web/apps/photos/src/pages/cluster-debug.tsx
View File

@@ -114,7 +114,9 @@ const OptionsForm: React.FC<OptionsFormProps> = ({ onCluster }) => {
minScore: 0.8,
minClusterSize: 2,
joinThreshold: 0.7,
batchSize: 12500,
earlyExitThreshold: 0.2,
batchSize: 10000,
lookbackSize: 2500,
},
onSubmit: (values) =>
onCluster({
@@ -123,7 +125,9 @@ const OptionsForm: React.FC<OptionsFormProps> = ({ onCluster }) => {
minScore: toFloat(values.minScore),
minClusterSize: toFloat(values.minClusterSize),
joinThreshold: toFloat(values.joinThreshold),
earlyExitThreshold: toFloat(values.earlyExitThreshold),
batchSize: toFloat(values.batchSize),
lookbackSize: toFloat(values.lookbackSize),
}),
});
@@ -171,6 +175,12 @@ const OptionsForm: React.FC<OptionsFormProps> = ({ onCluster }) => {
size="small"
onChange={handleChange}
/>
</Stack>
<Stack
direction="row"
gap={1}
sx={{ ".MuiFormControl-root": { flex: "1" } }}
>
<TextField
name="joinThreshold"
label="joinThreshold"
@@ -178,6 +188,13 @@ const OptionsForm: React.FC<OptionsFormProps> = ({ onCluster }) => {
size="small"
onChange={handleChange}
/>
<TextField
name="earlyExitThreshold"
label="earlyExitThreshold"
value={values.earlyExitThreshold}
size="small"
onChange={handleChange}
/>
<TextField
name="batchSize"
label="batchSize"
@@ -185,6 +202,13 @@ const OptionsForm: React.FC<OptionsFormProps> = ({ onCluster }) => {
size="small"
onChange={handleChange}
/>
<TextField
name="lookbackSize"
label="lookbackSize"
value={values.lookbackSize}
size="small"
onChange={handleChange}
/>
</Stack>
<Box marginInlineStart={"auto"} p={1}>
<Button
@@ -233,7 +257,7 @@ const ClusterList: React.FC<React.PropsWithChildren<ClusterListProps>> = ({
const itemSize = (index: number) =>
index === 0
? 140
? 200
: index === 1
? 130
: Array.isArray(items[index - 2])

52
web/packages/new/photos/services/ml/cluster.ts
View File

@@ -119,8 +119,10 @@ export interface ClusteringOpts {
minBlur: number;
minScore: number;
minClusterSize: number;
batchSize: number;
joinThreshold: number;
earlyExitThreshold: number;
batchSize: number;
lookbackSize: number;
}
export interface ClusterPreview {
@@ -182,11 +184,13 @@ export const clusterFaces = (
) => {
const {
method,
batchSize,
minBlur,
minScore,
minClusterSize,
joinThreshold,
earlyExitThreshold,
batchSize,
lookbackSize,
} = opts;
const t = Date.now();
@@ -258,6 +262,7 @@ export const clusterFaces = (
({ clusters: embeddingClusters } = clusterLinear(
embeddingBatch,
joinThreshold,
earlyExitThreshold,
));
}
@@ -296,10 +301,7 @@ export const clusterFaces = (
// Use a higher cosine similarity threshold if either of the two
// faces are blurry.
const threshold =
existingFace.blur < 200 || newFace.blur < 200
? 0.9
: joinThreshold;
const threshold = joinThreshold;
if (csim > threshold && csim > nnCosineSimilarity) {
nnCluster = existingCluster;
nnCosineSimilarity = csim;
@@ -510,7 +512,8 @@ export const clusterLinear_Direct = (
const clusterLinear = (
embeddings: number[][],
threshold: number,
joinThreshold: number,
earlyExitThreshold: number,
): ClusterLinearResult => {
const clusters: EmbeddingCluster[] = [];
const clusterIndexForEmbeddingIndex = new Map<number, number>();
@@ -534,7 +537,7 @@ const clusterLinear = (
// The vectors are already normalized, so we can directly use their
// dot product as their cosine similarity.
const csim = dotProduct(ei, ej);
if (csim > threshold) {
if (csim > joinThreshold) {
if (csim > nnCosineSimilarity) {
nnIndex = j;
nnCosineSimilarity = csim;
@@ -546,38 +549,25 @@ const clusterLinear = (
nClusterCosineSimilarity = csim;
}
}
// If we've found something "near enough", stop looking for a
// better match. This speeds up clustering.
if (csim < earlyExitThreshold) break;
}
}
if (nClusterIndex) {
// Found a neighbouring cluster close enough, add ourselves to that.
ensure(clusters[nClusterIndex]).push(i);
clusterIndexForEmbeddingIndex.set(i, nClusterIndex);
} else if (nnIndex) {
// Find the cluster the nearest neighbour belongs to, if any.
const nnClusterIndex = clusterIndexForEmbeddingIndex.get(nnIndex);
if (nnClusterIndex) {
// TODO-Cluster remove this case.
// If the neighbour is already part of a cluster, also add
// ourselves to that cluster.
// ensure(clusters[nnClusterIndex]).push(i);
// clusterIndexForEmbeddingIndex.set(i, nnClusterIndex);
throw new Error("We shouldn't have reached here");
} else {
// Otherwise create a new cluster with us and our nearest
// neighbour.
clusterIndexForEmbeddingIndex.set(i, clusters.length);
clusterIndexForEmbeddingIndex.set(nnIndex, clusters.length);
clusters.push([i, nnIndex]);
}
// Otherwise create a new cluster with us and our nearest neighbour.
clusterIndexForEmbeddingIndex.set(i, clusters.length);
clusterIndexForEmbeddingIndex.set(nnIndex, clusters.length);
clusters.push([i, nnIndex]);
} else {
// We didn't find a neighbour within the threshold. Create a new
// cluster with only this embedding.
// We didn't find a cluster or a neighbouring face within the
// threshold. Create a new cluster with only this embedding.
clusterIndexForEmbeddingIndex.set(i, clusters.length);
clusters.push([i]);
}