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Embeddings normalization fixes (#14284)

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* Use cosine distance metric for vec tables

* Only apply normalization to multi modal searches

* Catch possible edge case in stddev calc

* Use sigmoid function for normalization for multi modal searches only

* Ensure we get model state on initial page load

* Only save stats for multi modal searches and only use cosine similarity for image -> image search
This commit is contained in:
Josh Hawkins
2024-10-11 13:11:11 -05:00
committed by GitHub
parent d4b9b5a7dd
commit 8a8a0c7dec
5 changed files with 41 additions and 26 deletions
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33
frigate/api/event.py
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@@ -473,12 +473,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
)
thumb_result = context.search_thumbnail(search_event)
thumb_ids = dict(
zip(
[result[0] for result in thumb_result],
context.thumb_stats.normalize([result[1] for result in thumb_result]),
)
)
thumb_ids = {result[0]: result[1] for result in thumb_result}
search_results = {
event_id: {"distance": distance, "source": "thumbnail"}
for event_id, distance in thumb_ids.items()
@@ -486,15 +481,18 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
else:
search_types = search_type.split(",")
# only save stats for multi-modal searches
save_stats = "thumbnail" in search_types and "description" in search_types
if "thumbnail" in search_types:
thumb_result = context.search_thumbnail(query)
thumb_distances = context.thumb_stats.normalize(
[result[1] for result in thumb_result], save_stats
)
thumb_ids = dict(
zip(
[result[0] for result in thumb_result],
context.thumb_stats.normalize(
[result[1] for result in thumb_result]
),
)
zip([result[0] for result in thumb_result], thumb_distances)
)
search_results.update(
{
@@ -505,12 +503,13 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
if "description" in search_types:
desc_result = context.search_description(query)
desc_ids = dict(
zip(
[result[0] for result in desc_result],
context.desc_stats.normalize([result[1] for result in desc_result]),
)
desc_distances = context.desc_stats.normalize(
[result[1] for result in desc_result], save_stats
)
desc_ids = dict(zip([result[0] for result in desc_result], desc_distances))
for event_id, distance in desc_ids.items():
if (
event_id not in search_results