disable by default

machine-learning/app/config.py

@@ -21,6 +21,8 @@ class Settings(BaseSettings):
     request_threads: int = os.cpu_count() or 4
     model_inter_op_threads: int = 1
     model_intra_op_threads: int = 2
+    max_batch_size: int = 1
+    batch_timeout_s: float = 0.005
 
     class Config:
         env_prefix = "MACHINE_LEARNING_"

machine-learning/app/main.py

@@ -11,6 +11,7 @@ from onnxruntime.capi.onnxruntime_pybind11_state import InvalidProtobuf, NoSuchF
 from starlette.formparsers import MultiPartParser
 
 from app.models.base import InferenceModel
+from app.models.batcher import Batcher, ModelBatcher
 
 from .config import log, settings
 from .models.cache import ModelCache
@@ -26,6 +27,7 @@ app = FastAPI()
 
 def init_state() -> None:
     app.state.model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
+    app.state.model_batcher = ModelBatcher(max_size=settings.max_batch_size, timeout_s=settings.batch_timeout_s)
     log.info(
         (
             "Created in-memory cache with unloading "
@@ -62,9 +64,9 @@ async def predict(
     image: UploadFile | None = None,
 ) -> Any:
     if image is not None:
-        inputs: str | bytes = await image.read()
+        element: str | bytes = await image.read()
     elif text is not None:
-        inputs = text
+        element = text
     else:
         raise HTTPException(400, "Either image or text must be provided")
     try:
@@ -74,15 +76,21 @@ async def predict(
 
     model = await load(await app.state.model_cache.get(model_name, model_type, **kwargs))
     model.configure(**kwargs)
-    outputs = await run(model, inputs)
+
+    if settings.max_batch_size > 1:
+        batcher: Batcher = app.state.model_batcher.get(model_name, model_type, **kwargs)
+        outputs = await batcher.batch_process(element, run, model)
+    else:
+        outputs = await run(model, [element])
+
     return ORJSONResponse(outputs)
 
 
-async def run(model: InferenceModel, inputs: Any) -> Any:
+async def run(model: InferenceModel, elements: list[Any]) -> Any:
     if app.state.thread_pool is None:
-        return model.predict(inputs)
+        return model.predict_batch(elements)
 
-    return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict, inputs)
+    return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict_batch, elements)
 
 
 async def load(model: InferenceModel) -> InferenceModel:

machine-learning/app/models/base.py

@@ -12,6 +12,10 @@ from ..config import get_cache_dir, log, settings
 from ..schemas import ModelType
 
 
+def get_model_key(model_name: str, model_type: ModelType, mode: str = "") -> str:
+    return f"{model_name}{model_type.value}{mode}"
+
+
 class InferenceModel(ABC):
     _model_type: ModelType
 
@@ -65,14 +69,15 @@ class InferenceModel(ABC):
         self._load()
         self.loaded = True
 
-    def predict(self, inputs: Any, **model_kwargs: Any) -> Any:
+    def predict(self, element: Any) -> Any:
+        return self.predict_batch([element])[0]
+    
+    def predict_batch(self, inputs: list[Any]) -> list[Any]:
         self.load()
-        if model_kwargs:
-            self.configure(**model_kwargs)
-        return self._predict(inputs)
+        return self._predict_batch(inputs)
 
     @abstractmethod
-    def _predict(self, inputs: Any) -> Any:
+    def _predict_batch(self, inputs: list[Any]) -> Any:
         ...
 
     def configure(self, **model_kwargs: Any) -> None:

machine-learning/app/models/batcher.py

@@ -0,0 +1,65 @@
+import asyncio
+import time
+from typing import Any, Awaitable, Callable, TypeVar
+
+from app.schemas import ModelType
+
+from .base import get_model_key, log
+
+
+F = TypeVar("F")
+P = TypeVar("P")
+R = TypeVar("R")
+
+
+class Batcher:
+    def __init__(self, max_size: int = 16, timeout_s: float = 0.005) -> None:
+        self.max_size = max_size
+        self.timeout_s = timeout_s
+        self.queue: asyncio.Queue[Any] = asyncio.Queue(maxsize=max_size)
+        self.lock = asyncio.Lock()
+        self.processing: dict[int, Any] = {}
+        self.processed: dict[int, Any] = {}
+        self.element_id = 0
+
+    async def batch_process(self, element: Any, func: Callable[[list[P]], Awaitable[list[R]]], *args: Any, **kwargs: Any) -> Any:
+        cur_idx = self.element_id
+        self.element_id += 1
+        self.processing[cur_idx] = element
+        await self.queue.put(cur_idx)
+        try:
+            async with self.lock:
+                await self._batch(cur_idx)
+                if cur_idx not in self.processed:
+                    await self._process(func, *args, **kwargs)
+            return self.processed.pop(cur_idx)
+        finally:
+            self.processing.pop(cur_idx, None)
+            self.processed.pop(cur_idx, None)
+    
+    async def _batch(self, idx: int) -> list[Any]:
+        if idx not in self.processed:
+            start = time.monotonic()
+            while self.queue.qsize() < self.max_size and time.monotonic() - start < self.timeout_s:
+                await asyncio.sleep(0)
+
+    async def _process(self, func: Callable[[list[P]], Awaitable[list[R]]], *args, **kwargs) -> None:
+        batch_ids = [self.queue.get_nowait() for _ in range(self.queue.qsize())]
+        batch = [self.processing.pop(id) for id in batch_ids]
+        outputs = await func(*args, batch, **kwargs)
+        for id, output in zip(batch_ids, outputs):
+            self.processed[id] = output
+
+
+class ModelBatcher:
+    def __init__(self, max_size: int = 16, timeout_s: float = 0.005) -> None:
+        self.batchers = {}
+        self.max_size = max_size
+        self.timeout_s = timeout_s
+
+    def get(self, model_name: str, model_type: ModelType, **model_kwargs: Any):
+        key = get_model_key(model_name, model_type, model_kwargs.get("mode", ""))
+        if key not in self.batchers:
+            self.batchers[key] = Batcher(max_size=self.max_size, timeout_s=self.timeout_s)
+        
+        return self.batchers[key]

machine-learning/app/models/cache.py

@@ -5,7 +5,7 @@ from aiocache.lock import OptimisticLock
 from aiocache.plugins import BasePlugin, TimingPlugin
 
 from ..schemas import ModelType
-from .base import InferenceModel
+from .base import InferenceModel, get_model_key
 
 
 class ModelCache:
@@ -46,7 +46,7 @@ class ModelCache:
             model: The requested model.
         """
 
-        key = f"{model_name}{model_type.value}{model_kwargs.get('mode', '')}"
+        key = get_model_key(model_name, model_type, model_kwargs.get("mode", ""))
         async with OptimisticLock(self.cache, key) as lock:
             model = await self.cache.get(key)
             if model is None:

machine-learning/app/models/clip.py

@@ -1,8 +1,9 @@
 import os
 import zipfile
 from io import BytesIO
-from typing import Any, Literal
+from typing import Any, Literal, TypeGuard
 
+import numpy as np
 import onnxruntime as ort
 import torch
 from clip_server.model.clip import BICUBIC, _convert_image_to_rgb
@@ -17,6 +18,14 @@ from ..schemas import ModelType
 from .base import InferenceModel
 
 
+def is_image_list(images: list[Any]) -> TypeGuard[list[Image.Image | bytes]]:
+    return any(isinstance(image, (Image.Image, bytes)) for image in images)
+
+
+def is_text_list(texts: list[Any]) -> TypeGuard[list[str]]:
+    return any(isinstance(text, str) for text in texts)
+
+
 class CLIPEncoder(InferenceModel):
     _model_type = ModelType.CLIP
 
@@ -70,31 +79,42 @@ class CLIPEncoder(InferenceModel):
             image_size = _VISUAL_MODEL_IMAGE_SIZE[CLIPOnnxModel.get_model_name(self.model_name)]
             self.transform = _transform_pil_image(image_size)
 
-    def _predict(self, image_or_text: Image.Image | str) -> list[float]:
-        if isinstance(image_or_text, bytes):
-            image_or_text = Image.open(BytesIO(image_or_text))
+    def _predict_batch(self, images_or_text: list[Image.Image | bytes] | list[str]) -> list[list[float]]:
+        if not images_or_text:
+            return []
 
-        match image_or_text:
-            case Image.Image():
-                if self.mode == "text":
-                    raise TypeError("Cannot encode image as text-only model")
-                pixel_values = self.transform(image_or_text)
-                assert isinstance(pixel_values, torch.Tensor)
-                pixel_values = torch.unsqueeze(pixel_values, 0).numpy()
+        if is_image_list(images_or_text):
+            outputs = self._predict_images(images_or_text)
+        elif is_text_list(images_or_text):
+            outputs = self._predict_text(images_or_text)
+        else:
+            raise TypeError(f"Expected list of images or text, but got: {type(images_or_text[0])}")
+
+        return outputs
+
+    def _predict_images(self, images: list[Image.Image | bytes]) -> list[list[float]]:
+        if not images:
+            return []
+
+        for i, element in enumerate(images):
+            if isinstance(element, bytes):
+                images[i] = Image.open(BytesIO(element))
+
+        pixel_values = torch.stack([self.transform(image) for image in images]).numpy()
         outputs = self.vision_model.run(self.vision_outputs, {"pixel_values": pixel_values})
-            case str():
-                if self.mode == "vision":
-                    raise TypeError("Cannot encode text as vision-only model")
-                text_inputs: dict[str, torch.Tensor] = self.tokenizer(image_or_text)
+        return outputs[0].tolist()
+
+    def _predict_text(self, texts: list[str]) -> list[list[float]]:
+        if not texts:
+            return []
+
+        text_inputs: dict[str, torch.Tensor] = self.tokenizer(texts)
         inputs = {
             "input_ids": text_inputs["input_ids"].int().numpy(),
             "attention_mask": text_inputs["attention_mask"].int().numpy(),
         }
         outputs = self.text_model.run(self.text_outputs, inputs)
-            case _:
-                raise TypeError(f"Expected Image or str, but got: {type(image_or_text)}")
-
-        return outputs[0][0].tolist()
+        return outputs[0].tolist()
 
     def _download_model(self, model_name: str, model_md5: str) -> bool:
         # downloading logic is adapted from clip-server's CLIPOnnxModel class

machine-learning/app/models/facial_recognition.py

@@ -9,9 +9,12 @@ from insightface.model_zoo import ArcFaceONNX, RetinaFace
 from insightface.utils.face_align import norm_crop
 from insightface.utils.storage import BASE_REPO_URL, download_file
 
-from ..schemas import ModelType
+from ..schemas import ModelType, ndarray
 from .base import InferenceModel
 
+import onnx
+from onnx.tools import update_model_dims
+
 
 class FaceRecognizer(InferenceModel):
     _model_type = ModelType.FACIAL_RECOGNITION
@@ -36,6 +39,8 @@ class FaceRecognizer(InferenceModel):
             zip.extractall(self.cache_dir, members=[det_file, rec_file])
         zip_file.unlink()
 
+        self._add_batch_dimension(self.cache_dir / rec_file)
+
     def _load(self) -> None:
         try:
             det_file = next(self.cache_dir.glob("det_*.onnx"))
@@ -43,29 +48,36 @@ class FaceRecognizer(InferenceModel):
         except StopIteration:
             raise FileNotFoundError("Facial recognition models not found in cache directory")
 
-        self.det_model = RetinaFace(
-            session=ort.InferenceSession(
+        det_session = ort.InferenceSession(
             det_file.as_posix(),
             sess_options=self.sess_options,
             providers=self.providers,
             provider_options=self.provider_options,
-            ),
         )
-        self.rec_model = ArcFaceONNX(
-            rec_file.as_posix(),
-            session=ort.InferenceSession(
-                rec_file.as_posix(),
-                sess_options=self.sess_options,
-                providers=self.providers,
-                provider_options=self.provider_options,
-            ),
-        )
-
+        self.det_model = RetinaFace(session=det_session)
         self.det_model.prepare(
             ctx_id=0,
             det_thresh=self.min_score,
             input_size=(640, 640),
         )
+
+        rec_session = ort.InferenceSession(
+            rec_file.as_posix(),
+            sess_options=self.sess_options,
+            providers=self.providers,
+            provider_options=self.provider_options,
+        )
+        print(rec_session.get_inputs())
+        if rec_session.get_inputs()[0].shape[0] != "batch":
+            del rec_session
+            self._add_batch_dimension(rec_file)
+            rec_session = ort.InferenceSession(
+                rec_file.as_posix(),
+                sess_options=self.sess_options,
+                providers=self.providers,
+                provider_options=self.provider_options,
+            )
+        self.rec_model = ArcFaceONNX(rec_file.as_posix(), session=rec_session)
         self.rec_model.prepare(ctx_id=0)
 
     def _predict(self, image: np.ndarray[int, np.dtype[Any]] | bytes) -> list[dict[str, Any]]:
@@ -100,6 +112,90 @@ class FaceRecognizer(InferenceModel):
             )
         return results
 
+    def _predict_batch(self, images: list[cv2.Mat]) -> list[list[dict[str, Any]]]:
+        for i, image in enumerate(images):
+            if isinstance(image, bytes):
+                images[i] = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
+
+        batch_det, batch_kpss = self._detect(images)
+        batch_cropped_images, batch_offsets = self._preprocess(images, batch_kpss)
+        if batch_cropped_images:
+            batch_embeddings = self.rec_model.get_feat(images)
+            results = self._postprocess(images, batch_det, batch_embeddings, batch_offsets)
+        else:
+            results = self._postprocess(images, batch_det)
+        return results
+
+    def _detect(self, images: list[cv2.Mat]) -> tuple[list[ndarray], ...]:
+        batch_det: list[ndarray] = []
+        batch_kpss: list[ndarray] = []
+        # detection model doesn't support batching, but recognition model does
+        for image in images:
+            bboxes, kpss = self.det_model.detect(image)
+            batch_det.append(bboxes)
+            batch_kpss.append(kpss)
+        return batch_det, batch_kpss
+
+    def _preprocess(self, images: list[cv2.Mat], batch_kpss: list[ndarray]) -> tuple[list[cv2.Mat], list[int]]:
+        batch_cropped_images = []
+        batch_offsets = []
+        total_faces = 0
+        for i, image in enumerate(images):
+            kpss = batch_kpss[i]
+            total_faces += kpss.shape[0]
+            batch_offsets.append(total_faces)
+            for kps in kpss:
+                batch_cropped_images.append(norm_crop(image, kps))
+        return batch_cropped_images, batch_offsets
+
+    def _postprocess(
+        self,
+        images: list[cv2.Mat],
+        batch_det: list[ndarray],
+        batch_embeddings: ndarray | None = None,
+        batch_offsets: list[int] | None = None,
+    ) -> list[list[dict[str, Any]]]:
+        if batch_embeddings is not None and batch_offsets is not None:
+            image_embeddings: list[ndarray] | None = np.array_split(batch_embeddings, batch_offsets)
+        else:
+            image_embeddings = None
+
+        batch_faces: list[list[dict[str, Any]]] = []
+        for i, image in enumerate(images):
+            faces: list[dict[str, Any]] = []
+            batch_faces.append(faces)
+            if image_embeddings is None or image_embeddings[i].shape[0] == 0:
+                continue
+
+            height, width, _ = image.shape
+
+            embeddings = image_embeddings[i].tolist()
+            bboxes = batch_det[i][:, :4].round().tolist()
+            det_scores = batch_det[i][:, 4].tolist()
+            for (x1, y1, x2, y2), embedding, det_score in zip(bboxes, embeddings, det_scores):
+                face = {
+                    "imageWidth": width,
+                    "imageHeight": height,
+                    "boundingBox": {
+                        "x1": x1,
+                        "y1": y1,
+                        "x2": x2,
+                        "y2": y2,
+                    },
+                    "score": det_score,
+                    "embedding": embedding,
+                }
+
+                faces.append(face)
+        return batch_faces
+    
+    def _add_batch_dimension(self, model_path: Path) -> None:
+        rec_proto = onnx.load(model_path.as_posix())
+        inputs = {input.name: ['batch'] + [shape.dim_value for shape in input.type.tensor_type.shape.dim[1:]] for input in rec_proto.graph.input}
+        outputs = {output.name: ['batch'] + [shape.dim_value for shape in output.type.tensor_type.shape.dim[1:]] for output in rec_proto.graph.output}
+        rec_proto = update_model_dims.update_inputs_outputs_dims(rec_proto, inputs, outputs)
+        onnx.save(rec_proto, model_path.open("wb"))
+
     @property
     def cached(self) -> bool:
         return self.cache_dir.is_dir() and any(self.cache_dir.glob("*.onnx"))

machine-learning/app/models/image_classification.py

@@ -63,13 +63,18 @@ class ImageClassifier(InferenceModel):
                 feature_extractor=processor,
             )
 
-    def _predict(self, image: Image.Image | bytes) -> list[str]:
+    def _predict_batch(self, images: list[Image.Image | bytes]) -> list[list[str]]:
+        for i, image in enumerate(images):
             if isinstance(image, bytes):
-            image = Image.open(BytesIO(image))
-        predictions: list[dict[str, Any]] = self.model(image)  # type: ignore
-        tags = [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score]
+                images[i] = Image.open(BytesIO(image))
 
-        return tags
+        batch_predictions: list[list[dict[str, Any]]] = self.model(images)
+        results = [self._postprocess(predictions) for predictions in batch_predictions]
+
+        return results
+
+    def _postprocess(self, predictions: list[dict[str, Any]]) -> list[str]:
+        return [tag for pred in predictions for tag in pred["label"].split(", ") if pred["score"] >= self.min_score]
 
     def configure(self, **model_kwargs: Any) -> None:
         self.min_score = model_kwargs.pop("minScore", self.min_score)

machine-learning/app/schemas.py

@@ -1,5 +1,7 @@
 from enum import StrEnum
+from typing import Any, TypeAlias
 
+import numpy as np
 from pydantic import BaseModel
 
 
@@ -31,3 +33,6 @@ class ModelType(StrEnum):
     IMAGE_CLASSIFICATION = "image-classification"
     CLIP = "clip"
     FACIAL_RECOGNITION = "facial-recognition"
+
+
+ndarray: TypeAlias = np.ndarray[int, np.dtype[Any]]

machine-learning/app/test_main.py

@@ -22,16 +22,17 @@ from .schemas import ModelType
 ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
 
 
+@pytest.mark.asyncio
 class TestImageClassifier:
-    classifier_preds = [
+    classifier_preds = [[
         {"label": "that's an image alright", "score": 0.8},
         {"label": "well it ends with .jpg", "score": 0.1},
         {"label": "idk, im just seeing bytes", "score": 0.05},
         {"label": "not sure", "score": 0.04},
         {"label": "probably a virus", "score": 0.01},
-    ]
+    ]]
 
-    def test_min_score(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
+    async def test_min_score(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
         mocker.patch.object(ImageClassifier, "load")
         classifier = ImageClassifier("test_model_name", min_score=0.0)
         assert classifier.min_score == 0.0
@@ -39,9 +40,9 @@ class TestImageClassifier:
         classifier.model = mock.Mock()
         classifier.model.return_value = self.classifier_preds
 
-        all_labels = classifier.predict(pil_image)
+        all_labels = await classifier.predict(pil_image)
         classifier.min_score = 0.5
-        filtered_labels = classifier.predict(pil_image)
+        filtered_labels = await classifier.predict(pil_image)
 
         assert all_labels == [
             "that's an image alright",
@@ -54,29 +55,30 @@ class TestImageClassifier:
         assert filtered_labels == ["that's an image alright"]
 
 
+@pytest.mark.asyncio
 class TestCLIP:
-    embedding = np.random.rand(512).astype(np.float32)
+    embedding = np.random.rand(1, 512).astype(np.float32)
 
-    def test_basic_image(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
+    async def test_basic_image(self, pil_image: Image.Image, mocker: MockerFixture) -> None:
         mocker.patch.object(CLIPEncoder, "download")
         mocked = mocker.patch("app.models.clip.ort.InferenceSession", autospec=True)
-        mocked.return_value.run.return_value = [[self.embedding]]
+        mocked.return_value.run.return_value = [self.embedding]
         clip_encoder = CLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="vision")
         assert clip_encoder.mode == "vision"
-        embedding = clip_encoder.predict(pil_image)
+        embedding = await clip_encoder.predict(pil_image)
 
         assert isinstance(embedding, list)
         assert len(embedding) == 512
         assert all([isinstance(num, float) for num in embedding])
         clip_encoder.vision_model.run.assert_called_once()
 
-    def test_basic_text(self, mocker: MockerFixture) -> None:
+    async def test_basic_text(self, mocker: MockerFixture) -> None:
         mocker.patch.object(CLIPEncoder, "download")
         mocked = mocker.patch("app.models.clip.ort.InferenceSession", autospec=True)
-        mocked.return_value.run.return_value = [[self.embedding]]
+        mocked.return_value.run.return_value = [self.embedding]
         clip_encoder = CLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="text")
         assert clip_encoder.mode == "text"
-        embedding = clip_encoder.predict("test search query")
+        embedding = await clip_encoder.predict("test search query")
 
         assert isinstance(embedding, list)
         assert len(embedding) == 512
@@ -91,7 +93,8 @@ class TestFaceRecognition:
 
         assert face_recognizer.min_score == 0.5
 
-    def test_basic(self, cv_image: cv2.Mat, mocker: MockerFixture) -> None:
+    @pytest.mark.asyncio
+    async def test_basic(self, cv_image: cv2.Mat, mocker: MockerFixture) -> None:
         mocker.patch.object(FaceRecognizer, "load")
         face_recognizer = FaceRecognizer("test_model_name", min_score=0.0, cache_dir="test_cache")
 
@@ -108,7 +111,7 @@ class TestFaceRecognition:
         rec_model.get_feat.return_value = embedding
         face_recognizer.rec_model = rec_model
 
-        faces = face_recognizer.predict(cv_image)
+        faces = await face_recognizer.predict(cv_image)
 
         assert len(faces) == num_faces
         for face in faces:
@@ -119,7 +122,7 @@ class TestFaceRecognition:
             assert all([isinstance(num, float) for num in face["embedding"]])
 
         det_model.detect.assert_called_once()
-        assert rec_model.get_feat.call_count == num_faces
+        rec_model.get_feat.assert_called_once()
 
 
 @pytest.mark.asyncio