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Nicolas MowenandGitHub
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* Pin ruff * Add python upgrade fixes This enables python upgrade checks in ruff to look for deprecated types and patterns. This namely fixes: - usage of deprecated `Typing` which is now built in - some specific exceptions which are caught and have new aliases Some specific UP checks were also ignored as they are stylistic / unimportant and likely to cause bugs * Remove async blocking calls Use asyncio.to_thread on two remaining blocking calls to fix hanging event thread loop. Enable this specific rule to block it in the future. * Use proper logging mechanism * Correctly format logs * Raise with context When raising an exception include the from context to improve debugging * Cleanup
53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import logging
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from typing import Literal
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from pydantic import ConfigDict, Field
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.log import suppress_stderr_during
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from ..detector_utils import tflite_detect_raw, tflite_init
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try:
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from tflite_runtime.interpreter import Interpreter
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except ModuleNotFoundError:
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from ai_edge_litert.interpreter import Interpreter
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "cpu"
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class CpuDetectorConfig(BaseDetectorConfig):
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"""CPU TFLite detector that runs TensorFlow Lite models on the host CPU without hardware acceleration. Not recommended."""
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model_config = ConfigDict(
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title="CPU",
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)
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type: Literal[DETECTOR_KEY]
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num_threads: int = Field(
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default=3,
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title="Number of detection threads",
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description="The number of threads used for CPU-based inference.",
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)
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class CpuTfl(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: CpuDetectorConfig):
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# Suppress TFLite delegate creation messages that bypass Python logging
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with suppress_stderr_during("tflite_interpreter_init"):
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interpreter = Interpreter(
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model_path=detector_config.model.path,
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num_threads=detector_config.num_threads or 3,
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)
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tflite_init(self, interpreter)
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def detect_raw(self, tensor_input):
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return tflite_detect_raw(self, tensor_input)
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