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The fallback to tensorflow was established back in 2023, because we could not provide tflite-runtime downstream in nixpkgs. By now we have ai-edge-litert available, which is the successor to the tflite-runtime. It still provides the same entrypoints as tflite-runtime and functionality has been verified in multiple deployments for the last two weeks.
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
import logging
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from pydantic import Field
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from typing_extensions import Literal
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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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type: Literal[DETECTOR_KEY]
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num_threads: int = Field(default=3, title="Number of detection threads")
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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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