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chore(ml): installable package (#17153)

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* app -> immich_ml

* fix test ci

* omit file name

* add new line

* add new line
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Mert 2025-03-27 15:49:09 -04:00 • committed by GitHub
parent f7d730eb05
commit 84c35e35d6
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31 changed files with 347 additions and 316 deletions
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119
machine-learning/app/schemas.py
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@ -1,119 +0,0 @@
from enum import Enum
from typing import Any, Literal, Protocol, TypeGuard, TypeVar
import numpy as np
import numpy.typing as npt
from typing_extensions import TypedDict
class StrEnum(str, Enum):
value: str
def __str__(self) -> str:
return self.value
class BoundingBox(TypedDict):
x1: int
y1: int
x2: int
y2: int
class ModelTask(StrEnum):
FACIAL_RECOGNITION = "facial-recognition"
SEARCH = "clip"
class ModelType(StrEnum):
DETECTION = "detection"
RECOGNITION = "recognition"
TEXTUAL = "textual"
VISUAL = "visual"
class ModelFormat(StrEnum):
ARMNN = "armnn"
ONNX = "onnx"
RKNN = "rknn"
class ModelSource(StrEnum):
INSIGHTFACE = "insightface"
MCLIP = "mclip"
OPENCLIP = "openclip"
ModelIdentity = tuple[ModelType, ModelTask]
class SessionNode(Protocol):
@property
def name(self) -> str | None: ...
@property
def shape(self) -> tuple[int, ...]: ...
class ModelSession(Protocol):
def run(
self,
output_names: list[str] | None,
input_feed: dict[str, npt.NDArray[np.float32]] | dict[str, npt.NDArray[np.int32]],
run_options: Any = None,
) -> list[npt.NDArray[np.float32]]: ...
def get_inputs(self) -> list[SessionNode]: ...
def get_outputs(self) -> list[SessionNode]: ...
class HasProfiling(Protocol):
profiling: dict[str, float]
class FaceDetectionOutput(TypedDict):
boxes: npt.NDArray[np.float32]
scores: npt.NDArray[np.float32]
landmarks: npt.NDArray[np.float32]
class DetectedFace(TypedDict):
boundingBox: BoundingBox
embedding: str
score: float
FacialRecognitionOutput = list[DetectedFace]
class PipelineEntry(TypedDict):
modelName: str
options: dict[str, Any]
PipelineRequest = dict[ModelTask, dict[ModelType, PipelineEntry]]
class InferenceEntry(TypedDict):
name: str
task: ModelTask
type: ModelType
options: dict[str, Any]
InferenceEntries = tuple[list[InferenceEntry], list[InferenceEntry]]
InferenceResponse = dict[ModelTask | Literal["imageHeight"] | Literal["imageWidth"], Any]
def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
def is_ndarray(obj: Any, dtype: "type[np._DTypeScalar_co]") -> "TypeGuard[npt.NDArray[np._DTypeScalar_co]]":
return isinstance(obj, np.ndarray) and obj.dtype == dtype
T = TypeVar("T")