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feat(ml)!: customizable ML settings (#3891)

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* consolidated endpoints, added live configuration

* added ml settings to server

* added settings dashboard

* updated deps, fixed typos

* simplified modelconfig

updated tests

* Added ml setting accordion for admin page

updated tests

* merge `clipText` and `clipVision`

* added face distance setting

clarified setting

* add clip mode in request, dropdown for face models

* polished ml settings

updated descriptions

* update clip field on error

* removed unused import

* add description for image classification threshold

* pin safetensors for arm wheel

updated poetry lock

* moved dto

* set model type only in ml repository

* revert form-data package install

use fetch instead of axios

* added slotted description with link

updated facial recognition description

clarified effect of disabling tasks

* validation before model load

* removed unnecessary getconfig call

* added migration

* updated api

updated api

updated api

---------

Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
This commit is contained in:
Mert 2023-08-29 09:58:00 -04:00 • committed by GitHub
parent 22f5e05060
commit bcc36d14a1
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56 changed files with 2324 additions and 655 deletions
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12
machine-learning/app/models/facial_recognition.py
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@ -9,7 +9,6 @@ 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 ..config import settings
from ..schemas import ModelType
from .base import InferenceModel
@ -20,7 +19,7 @@ class FaceRecognizer(InferenceModel):
def __init__(
self,
model_name: str,
min_score: float = settings.min_face_score,
min_score: float = 0.7,
cache_dir: Path | str | None = None,
**model_kwargs: Any,
) -> None:
@ -69,11 +68,13 @@ class FaceRecognizer(InferenceModel):
)
self.rec_model.prepare(ctx_id=0)
def _predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
def _predict(self, image: np.ndarray[int, np.dtype[Any]] | bytes) -> list[dict[str, Any]]:
if isinstance(image, bytes):
image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
bboxes, kpss = self.det_model.detect(image)
if bboxes.size == 0:
return []
assert isinstance(kpss, np.ndarray)
assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray)
scores = bboxes[:, 4].tolist()
bboxes = bboxes[:, :4].round().tolist()
@ -102,3 +103,6 @@ class FaceRecognizer(InferenceModel):
@property
def cached(self) -> bool:
return self.cache_dir.is_dir() and any(self.cache_dir.glob("*.onnx"))
def configure(self, **model_kwargs: Any) -> None:
self.det_model.det_thresh = model_kwargs.get("min_score", self.det_model.det_thresh)