add generic onnx model class and use jina ai clip models for all embeddings

frigate/embeddings/__init__.py

@@ -73,7 +73,7 @@ class EmbeddingsContext:
     def __init__(self, db: SqliteVecQueueDatabase):
         self.embeddings = Embeddings(db)
         self.thumb_stats = ZScoreNormalization()
-        self.desc_stats = ZScoreNormalization(scale_factor=3, bias=-2.5)
+        self.desc_stats = ZScoreNormalization()
 
         # load stats from disk
         try:

frigate/embeddings/embeddings.py

@@ -7,6 +7,7 @@ import struct
 import time
 from typing import List, Tuple, Union
 
+import numpy as np
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -16,8 +17,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
 from frigate.types import ModelStatusTypesEnum
 
-from .functions.clip import ClipEmbedding
-from .functions.minilm_l6_v2 import MiniLMEmbedding
+from .functions.onnx import GenericONNXEmbedding
 
 logger = logging.getLogger(__name__)
 
@@ -53,9 +53,23 @@ def get_metadata(event: Event) -> dict:
     )
 
 
-def serialize(vector: List[float]) -> bytes:
-    """Serializes a list of floats into a compact "raw bytes" format"""
-    return struct.pack("%sf" % len(vector), *vector)
+def serialize(vector: Union[List[float], np.ndarray, float]) -> bytes:
+    """Serializes a list of floats, numpy array, or single float into a compact "raw bytes" format"""
+    if isinstance(vector, np.ndarray):
+        # Convert numpy array to list of floats
+        vector = vector.flatten().tolist()
+    elif isinstance(vector, (float, np.float32, np.float64)):
+        # Handle single float values
+        vector = [vector]
+    elif not isinstance(vector, list):
+        raise TypeError(
+            f"Input must be a list of floats, a numpy array, or a single float. Got {type(vector)}"
+        )
+
+    try:
+        return struct.pack("%sf" % len(vector), *vector)
+    except struct.error as e:
+        raise ValueError(f"Failed to pack vector: {e}. Vector: {vector}")
 
 
 def deserialize(bytes_data: bytes) -> List[float]:
@@ -74,10 +88,10 @@ class Embeddings:
         self._create_tables()
 
         models = [
-            "sentence-transformers/all-MiniLM-L6-v2-model.onnx",
-            "sentence-transformers/all-MiniLM-L6-v2-tokenizer",
-            "clip-clip_image_model_vitb32.onnx",
-            "clip-clip_text_model_vitb32.onnx",
+            "all-jina-clip-v1-text_model_fp16.onnx",
+            "all-jina-clip-v1-tokenizer",
+            "all-jina-clip-v1-vision_model_fp16.onnx",
+            "all-jina-clip-v1-preprocessor_config.json",
         ]
 
         for model in models:
@@ -89,11 +103,32 @@ class Embeddings:
                 },
             )
 
-        self.clip_embedding = ClipEmbedding(
-            preferred_providers=["CPUExecutionProvider"]
+        def jina_text_embedding_function(outputs):
+            return outputs[0]
+
+        def jina_vision_embedding_function(outputs):
+            return outputs[0]
+
+        self.text_embedding = GenericONNXEmbedding(
+            model_name="all-jina-clip-v1",
+            model_file="text_model_fp16.onnx",
+            tokenizer_file="tokenizer",
+            download_urls={
+                "text_model_fp16.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/text_model_fp16.onnx"
+            },
+            embedding_function=jina_text_embedding_function,
+            model_type="text",
         )
-        self.minilm_embedding = MiniLMEmbedding(
-            preferred_providers=["CPUExecutionProvider"],
+
+        self.vision_embedding = GenericONNXEmbedding(
+            model_name="all-jina-clip-v1",
+            model_file="vision_model_fp16.onnx",
+            tokenizer_file="preprocessor_config.json",
+            download_urls={
+                "vision_model_fp16.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/vision_model_fp16.onnx"
+            },
+            embedding_function=jina_vision_embedding_function,
+            model_type="vision",
         )
 
     def _create_tables(self):
@@ -101,7 +136,7 @@ class Embeddings:
         self.db.execute_sql("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                 id TEXT PRIMARY KEY,
-                thumbnail_embedding FLOAT[512]
+                thumbnail_embedding FLOAT[768]
             );
         """)
 
@@ -109,15 +144,14 @@ class Embeddings:
         self.db.execute_sql("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                 id TEXT PRIMARY KEY,
-                description_embedding FLOAT[384]
+                description_embedding FLOAT[768]
             );
         """)
 
     def upsert_thumbnail(self, event_id: str, thumbnail: bytes):
         # Convert thumbnail bytes to PIL Image
         image = Image.open(io.BytesIO(thumbnail)).convert("RGB")
-        # Generate embedding using CLIP
-        embedding = self.clip_embedding([image])[0]
+        embedding = self.vision_embedding([image])[0]
 
         self.db.execute_sql(
             """
@@ -130,8 +164,7 @@ class Embeddings:
         return embedding
 
     def upsert_description(self, event_id: str, description: str):
-        # Generate embedding using MiniLM
-        embedding = self.minilm_embedding([description])[0]
+        embedding = self.text_embedding([description])[0]
 
         self.db.execute_sql(
             """
@@ -177,7 +210,7 @@ class Embeddings:
                 thumbnail = base64.b64decode(query.thumbnail)
                 query_embedding = self.upsert_thumbnail(query.id, thumbnail)
         else:
-            query_embedding = self.clip_embedding([query])[0]
+            query_embedding = self.text_embedding([query])[0]
 
         sql_query = """
             SELECT
@@ -211,7 +244,7 @@ class Embeddings:
     def search_description(
         self, query_text: str, event_ids: List[str] = None
     ) -> List[Tuple[str, float]]:
-        query_embedding = self.minilm_embedding([query_text])[0]
+        query_embedding = self.text_embedding([query_text])[0]
 
         # Prepare the base SQL query
         sql_query = """

frigate/embeddings/functions/onnx.py

@@ -0,0 +1,171 @@
+import logging
+import os
+import warnings
+from io import BytesIO
+from typing import Callable, Dict, List, Union
+
+import numpy as np
+import onnxruntime as ort
+import requests
+from PIL import Image
+
+# importing this without pytorch or others causes a warning
+# https://github.com/huggingface/transformers/issues/27214
+# suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1
+from transformers import AutoFeatureExtractor, AutoTokenizer
+
+from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
+from frigate.types import ModelStatusTypesEnum
+from frigate.util.downloader import ModelDownloader
+
+warnings.filterwarnings(
+    "ignore",
+    category=FutureWarning,
+    message="The class CLIPFeatureExtractor is deprecated",
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class GenericONNXEmbedding:
+    """Generic embedding function for ONNX models (text and vision)."""
+
+    def __init__(
+        self,
+        model_name: str,
+        model_file: str,
+        tokenizer_file: str,
+        download_urls: Dict[str, str],
+        embedding_function: Callable[[List[np.ndarray]], np.ndarray],
+        model_type: str,
+        preferred_providers: List[str] = ["CPUExecutionProvider"],
+    ):
+        self.model_name = model_name
+        self.model_file = model_file
+        self.tokenizer_file = tokenizer_file
+        self.download_urls = download_urls
+        self.embedding_function = embedding_function
+        self.model_type = model_type  # 'text' or 'vision'
+        self.preferred_providers = preferred_providers
+
+        self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
+        self.tokenizer = None
+        self.feature_extractor = None
+        self.session = None
+
+        self.downloader = ModelDownloader(
+            model_name=self.model_name,
+            download_path=self.download_path,
+            file_names=[self.model_file, self.tokenizer_file],
+            download_func=self._download_model,
+        )
+        self.downloader.ensure_model_files()
+
+    def _download_model(self, path: str):
+        try:
+            file_name = os.path.basename(path)
+            if file_name in self.download_urls:
+                ModelDownloader.download_from_url(self.download_urls[file_name], path)
+            elif file_name == self.tokenizer_file:
+                logger.info(
+                    f"Downloading {self.model_name} tokenizer/feature extractor"
+                )
+                if self.model_type == "text":
+                    tokenizer = AutoTokenizer.from_pretrained(
+                        self.model_name, clean_up_tokenization_spaces=True
+                    )
+                    tokenizer.save_pretrained(path)
+                else:
+                    feature_extractor = AutoFeatureExtractor.from_pretrained(
+                        self.model_name
+                    )
+                    feature_extractor.save_pretrained(path)
+
+            self.downloader.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.model_name}-{file_name}",
+                    "state": ModelStatusTypesEnum.downloaded,
+                },
+            )
+        except Exception:
+            self.downloader.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.model_name}-{file_name}",
+                    "state": ModelStatusTypesEnum.error,
+                },
+            )
+
+    def _load_model_and_tokenizer(self):
+        if self.session is None:
+            self.downloader.wait_for_download()
+            if self.model_type == "text":
+                self.tokenizer = self._load_tokenizer()
+            else:
+                self.feature_extractor = self._load_feature_extractor()
+            self.session = self._load_model(
+                os.path.join(self.download_path, self.model_file),
+                self.preferred_providers,
+            )
+
+    def _load_tokenizer(self):
+        tokenizer_path = os.path.join(self.download_path, self.tokenizer_file)
+        return AutoTokenizer.from_pretrained(
+            tokenizer_path, clean_up_tokenization_spaces=True
+        )
+
+    def _load_feature_extractor(self):
+        feature_extractor_path = os.path.join(self.download_path, self.tokenizer_file)
+        return AutoFeatureExtractor.from_pretrained(feature_extractor_path)
+
+    def _load_model(self, path: str, providers: List[str]):
+        if os.path.exists(path):
+            return ort.InferenceSession(path, providers=providers)
+        else:
+            logger.warning(f"{self.model_name} model file {path} not found.")
+            return None
+
+    def _process_image(self, image):
+        if isinstance(image, str):
+            if image.startswith("http"):
+                response = requests.get(image)
+                image = Image.open(BytesIO(response.content)).convert("RGB")
+
+        return image
+
+    def __call__(
+        self, inputs: Union[List[str], List[Image.Image], List[str]]
+    ) -> List[np.ndarray]:
+        self._load_model_and_tokenizer()
+
+        if self.session is None or (
+            self.tokenizer is None and self.feature_extractor is None
+        ):
+            logger.error(
+                f"{self.model_name} model or tokenizer/feature extractor is not loaded."
+            )
+            return []
+
+        if self.model_type == "text":
+            processed_inputs = self.tokenizer(
+                inputs, padding=True, truncation=True, return_tensors="np"
+            )
+        else:
+            processed_images = [self._process_image(img) for img in inputs]
+            processed_inputs = self.feature_extractor(
+                images=processed_images, return_tensors="np"
+            )
+
+        input_names = [input.name for input in self.session.get_inputs()]
+        onnx_inputs = {
+            name: processed_inputs[name]
+            for name in input_names
+            if name in processed_inputs
+        }
+
+        outputs = self.session.run(None, onnx_inputs)
+        embeddings = self.embedding_function(outputs)
+
+        return [embedding for embedding in embeddings]

web/src/pages/Explore.tsx

@@ -184,31 +184,31 @@ export default function Explore() {
 
   // model states
 
-  const { payload: minilmModelState } = useModelState(
-    "sentence-transformers/all-MiniLM-L6-v2-model.onnx",
+  const { payload: textModelState } = useModelState(
+    "all-jina-clip-v1-text_model_fp16.onnx",
   );
-  const { payload: minilmTokenizerState } = useModelState(
-    "sentence-transformers/all-MiniLM-L6-v2-tokenizer",
+  const { payload: textTokenizerState } = useModelState(
+    "all-jina-clip-v1-tokenizer",
   );
-  const { payload: clipImageModelState } = useModelState(
-    "clip-clip_image_model_vitb32.onnx",
+  const { payload: visionModelState } = useModelState(
+    "all-jina-clip-v1-vision_model_fp16.onnx",
   );
-  const { payload: clipTextModelState } = useModelState(
-    "clip-clip_text_model_vitb32.onnx",
+  const { payload: visionFeatureExtractorState } = useModelState(
+    "all-jina-clip-v1-preprocessor_config.json",
   );
 
   const allModelsLoaded = useMemo(() => {
     return (
-      minilmModelState === "downloaded" &&
-      minilmTokenizerState === "downloaded" &&
-      clipImageModelState === "downloaded" &&
-      clipTextModelState === "downloaded"
+      textModelState === "downloaded" &&
+      textTokenizerState === "downloaded" &&
+      visionModelState === "downloaded" &&
+      visionFeatureExtractorState === "downloaded"
     );
   }, [
-    minilmModelState,
-    minilmTokenizerState,
-    clipImageModelState,
-    clipTextModelState,
+    textModelState,
+    textTokenizerState,
+    visionModelState,
+    visionFeatureExtractorState,
   ]);
 
   const renderModelStateIcon = (modelState: ModelState) => {
@@ -225,10 +225,10 @@ export default function Explore() {
   };
 
   if (
-    !minilmModelState ||
-    !minilmTokenizerState ||
-    !clipImageModelState ||
-    !clipTextModelState
+    !textModelState ||
+    !textTokenizerState ||
+    !visionModelState ||
+    !visionFeatureExtractorState
   ) {
     return (
       <ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />
@@ -251,25 +251,26 @@ export default function Explore() {
             </div>
             <div className="flex w-96 flex-col gap-2 py-5">
               <div className="flex flex-row items-center justify-center gap-2">
-                {renderModelStateIcon(clipImageModelState)}
-                CLIP image model
+                {renderModelStateIcon(visionModelState)}
+                Vision model
               </div>
               <div className="flex flex-row items-center justify-center gap-2">
-                {renderModelStateIcon(clipTextModelState)}
-                CLIP text model
+                {renderModelStateIcon(visionFeatureExtractorState)}
+                Vision model feature extractor
               </div>
               <div className="flex flex-row items-center justify-center gap-2">
-                {renderModelStateIcon(minilmModelState)}
-                MiniLM sentence model
+                {renderModelStateIcon(textModelState)}
+                Text model
               </div>
               <div className="flex flex-row items-center justify-center gap-2">
-                {renderModelStateIcon(minilmTokenizerState)}
-                MiniLM tokenizer
+                {renderModelStateIcon(textTokenizerState)}
+                Text tokenizer
               </div>
             </div>
-            {(minilmModelState === "error" ||
-              clipImageModelState === "error" ||
-              clipTextModelState === "error") && (
+            {(textModelState === "error" ||
+              textTokenizerState === "error" ||
+              visionModelState === "error" ||
+              visionFeatureExtractorState === "error") && (
               <div className="my-3 max-w-96 text-center text-danger">
                 An error has occurred. Check Frigate logs.
               </div>