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fixes

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This commit is contained in:
Josh Hawkins 2024-10-13 15:37:11 -05:00
parent 66d0ad5803
commit 7a458f6c2b
2 changed files with 25 additions and 15 deletions
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28
frigate/embeddings/embeddings.py
View File

@ -146,13 +146,14 @@ class Embeddings:
ids = list(event_thumbs.keys())
embeddings = self.vision_embedding(images)
items = [(ids[i], serialize(embeddings[i])) for i in range(len(ids))]
flat_items = [item for sublist in items for item in sublist]
self.db.execute_sql(
"""
INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
VALUES {}
""".format(", ".join(["(?, ?)"] * len(items))),
items,
flat_items,
)
return embeddings
@ -172,13 +173,14 @@ class Embeddings:
embeddings = self.text_embedding(list(event_descriptions.values()))
ids = list(event_descriptions.keys())
items = [(ids[i], serialize(embeddings[i])) for i in range(len(ids))]
flat_items = [item for sublist in items for item in sublist]
self.db.execute_sql(
"""
INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
VALUES {}
""".format(", ".join(["(?, ?)"] * len(items))),
items,
flat_items,
)
return embeddings
@ -196,16 +198,6 @@ class Embeddings:
os.remove(os.path.join(CONFIG_DIR, ".search_stats.json"))
st = time.time()
totals = {
"thumbnails": 0,
"descriptions": 0,
"processed_objects": 0,
"total_objects": 0,
"time_remaining": 0,
"status": "indexing",
}
self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
# Get total count of events to process
total_events = (
@ -216,11 +208,21 @@ class Embeddings:
)
.count()
)
totals["total_objects"] = total_events
batch_size = 32
current_page = 1
totals = {
"thumbnails": 0,
"descriptions": 0,
"processed_objects": total_events - 1 if total_events < batch_size else 0,
"total_objects": total_events,
"time_remaining": 0 if total_events < batch_size else -1,
"status": "indexing",
}
self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
events = (
Event.select()
.where(

12
frigate/embeddings/functions/onnx.py
View File

@ -164,8 +164,15 @@ class GenericONNXEmbedding:
return []
if self.model_type == "text":
max_length = max(len(self.tokenizer.encode(text)) for text in inputs)
processed_inputs = [
self.tokenizer(text, padding=True, truncation=True, return_tensors="np")
self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=max_length,
return_tensors="np",
)
for text in inputs
]
else:
@ -184,7 +191,8 @@ class GenericONNXEmbedding:
onnx_inputs[key].append(value[0])
for key in onnx_inputs.keys():
onnx_inputs[key] = np.array(onnx_inputs[key])
if onnx_inputs[key]:
onnx_inputs[key] = np.stack(onnx_inputs[key])
embeddings = self.runner.run(onnx_inputs)[0]
return [embedding for embedding in embeddings]