MertandGitHub
23f1f87dd0
* add immich_model * graph per shape * decouple ocr from ort * prepared graphs * use f16 for accelerators * preload all shapes * warm-up pass after preload * keep precision for fp32 graphs * ensure padding for text recognition * optimize postprocessing for fp16 outputs * handle raw ctc logits * use f16 output for rknn * mmap token embedding * read device metadata
146 lines
2.9 KiB
Python
146 lines
2.9 KiB
Python
from collections.abc import Mapping, Sequence
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from dataclasses import asdict, dataclass
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from enum import Enum
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from typing import Any, Literal, Protocol, TypeAlias, TypeVar
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import numpy as np
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import numpy.typing as npt
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from typing_extensions import TypedDict
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class StrEnum(str, Enum):
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__str__ = str.__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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OCR = "ocr"
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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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RKNN = "rknn"
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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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PADDLE = "paddle"
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class ModelOrganization(StrEnum):
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APP = "immich-app"
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TESTING = "immich-testing"
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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: ...
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@property
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def shape(self) -> tuple[int | str, ...]: ... # ORT names a symbolic dim rather than sizing it
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ModelTensor: TypeAlias = npt.NDArray[np.float32] | npt.NDArray[np.int32] | npt.NDArray[np.uint8]
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ModelInput = Mapping[str, ModelTensor]
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@dataclass(frozen=True)
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class Shape:
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batch: int
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height: int | None = None
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width: int | None = None
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@property
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def pins(self) -> dict[str, int]:
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return {name: size for name, size in asdict(self).items() if size is not None}
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class ModelGraph(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: ModelInput,
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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) -> Sequence[SessionNode]: ...
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def get_outputs(self) -> Sequence[SessionNode]: ...
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def get_metadata(self) -> dict[str, str]: ...
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@property
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def normalizes_input(self) -> bool: ...
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class ModelSession(Protocol):
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@property
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def shapes(self) -> tuple[Shape, ...]: ...
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@property
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def batches(self) -> tuple[int, ...]: ...
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def for_shape(self, shape: Shape) -> ModelGraph: ...
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def warm(self) -> None: ...
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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: str
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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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T = TypeVar("T")
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