mirror of
https://github.com/immich-app/immich.git
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126 lines
2.5 KiB
Python
126 lines
2.5 KiB
Python
from enum import Enum
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from typing import Any, Literal, Protocol, TypedDict, TypeGuard, TypeVar
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import numpy as np
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import numpy.typing as npt
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from pydantic import BaseModel
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class StrEnum(str, Enum):
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value: str
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def __str__(self) -> str:
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return self.value
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class TextResponse(BaseModel):
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__root__: str
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class MessageResponse(BaseModel):
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message: str
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class BoundingBox(TypedDict):
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x1: int
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y1: int
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x2: int
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y2: int
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class ModelTask(StrEnum):
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FACIAL_RECOGNITION = "facial-recognition"
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SEARCH = "clip"
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class ModelType(StrEnum):
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DETECTION = "detection"
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RECOGNITION = "recognition"
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TEXTUAL = "textual"
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VISUAL = "visual"
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class ModelFormat(StrEnum):
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ARMNN = "armnn"
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ONNX = "onnx"
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class ModelSource(StrEnum):
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INSIGHTFACE = "insightface"
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MCLIP = "mclip"
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OPENCLIP = "openclip"
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ModelIdentity = tuple[ModelType, ModelTask]
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class SessionNode(Protocol):
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@property
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def name(self) -> str | None: ...
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@property
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def shape(self) -> tuple[int, ...]: ...
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class ModelSession(Protocol):
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def run(
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self,
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output_names: list[str] | None,
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input_feed: dict[str, npt.NDArray[np.float32]] | dict[str, npt.NDArray[np.int32]],
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run_options: Any = None,
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) -> list[npt.NDArray[np.float32]]: ...
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def get_inputs(self) -> list[SessionNode]: ...
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def get_outputs(self) -> list[SessionNode]: ...
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class HasProfiling(Protocol):
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profiling: dict[str, float]
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class FaceDetectionOutput(TypedDict):
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boxes: npt.NDArray[np.float32]
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scores: npt.NDArray[np.float32]
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landmarks: npt.NDArray[np.float32]
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class DetectedFace(TypedDict):
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boundingBox: BoundingBox
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embedding: npt.NDArray[np.float32]
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score: float
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FacialRecognitionOutput = list[DetectedFace]
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class PipelineEntry(TypedDict):
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modelName: str
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options: dict[str, Any]
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PipelineRequest = dict[ModelTask, dict[ModelType, PipelineEntry]]
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class InferenceEntry(TypedDict):
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name: str
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task: ModelTask
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type: ModelType
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options: dict[str, Any]
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InferenceEntries = tuple[list[InferenceEntry], list[InferenceEntry]]
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InferenceResponse = dict[ModelTask | Literal["imageHeight"] | Literal["imageWidth"], Any]
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def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
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return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
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def is_ndarray(obj: Any, dtype: "type[np._DTypeScalar_co]") -> "TypeGuard[npt.NDArray[np._DTypeScalar_co]]":
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return isinstance(obj, np.ndarray) and obj.dtype == dtype
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T = TypeVar("T")
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