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	* cuda and openvino ep, refactor, update dockerfile * updated workflow * typing fixes * added tests * updated ml test gh action * updated README * updated docker-compose * added compute to hwaccel.yml * updated gh matrix updated gh matrix updated gh matrix updated gh matrix updated gh matrix give up * remove cuda/arm64 build * add hwaccel image tags to docker-compose * remove unnecessary quotes * add suffix to git tag * fixed kwargs in base model * armnn ld_library_path * update pyproject.toml * add armnn workflow * formatting * consolidate hwaccel files, update docker compose * update hw transcoding docs * add ml hwaccel docs * update dev and prod docker-compose * added armnn prerequisite docs * support 3.10 * updated docker-compose comments * formatting * test coverage * don't set arena extend strategy for openvino * working openvino * formatting * fix dockerfile * added type annotation * add wsl configuration for openvino * updated lock file * copy python3 * comment out extends section * fix platforms * simplify workflow suffix tagging * simplify aio transcoding doc * update docs and workflow for `hwaccel.yml` change * revert docs
		
			
				
	
	
		
			47 lines
		
	
	
		
			952 B
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			47 lines
		
	
	
		
			952 B
		
	
	
	
		
			Python
		
	
	
	
	
	
| from enum import Enum
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| from typing import Any, Protocol, TypedDict, TypeGuard
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| 
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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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| 
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| 
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| class TextResponse(BaseModel):
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|     __root__: str
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| 
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| 
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| class MessageResponse(BaseModel):
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|     message: str
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| 
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| 
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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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| 
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| 
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| class ModelType(str, Enum):
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|     CLIP = "clip"
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|     FACIAL_RECOGNITION = "facial-recognition"
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| 
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| 
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| class HasProfiling(Protocol):
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|     profiling: dict[str, float]
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| 
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| 
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| class Face(TypedDict):
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|     boundingBox: BoundingBox
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|     embedding: npt.NDArray[np.float32]
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|     imageWidth: int
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|     imageHeight: int
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|     score: float
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| 
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| 
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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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| 
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| 
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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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