mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
import torch
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import onnxruntime
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class DeviceManager(object):
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_instance = None
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forceTensor: bool = False
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@classmethod
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def get_instance(cls):
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if cls._instance is None:
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cls._instance = cls()
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return cls._instance
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def __init__(self):
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self.gpu_num = torch.cuda.device_count()
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self.mps_enabled: bool = (
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getattr(torch.backends, "mps", None) is not None
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and torch.backends.mps.is_available()
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)
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def getDevice(self, id: int):
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if id < 0 or self.gpu_num == 0:
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if self.mps_enabled is False:
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dev = torch.device("cpu")
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else:
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dev = torch.device("mps")
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else:
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if id < self.gpu_num:
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dev = torch.device("cuda", index=id)
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else:
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print("[Voice Changer] device detection error, fallback to cpu")
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dev = torch.device("cpu")
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return dev
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def getOnnxExecutionProvider(self, gpu: int):
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availableProviders = onnxruntime.get_available_providers()
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devNum = torch.cuda.device_count()
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if gpu >= 0 and "CUDAExecutionProvider" in availableProviders and devNum > 0:
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if gpu < devNum: # ひとつ前のif文で弾いてもよいが、エラーの解像度を上げるため一段下げ。
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return ["CUDAExecutionProvider"], [{"device_id": gpu}]
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else:
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print("[Voice Changer] device detection error, fallback to cpu")
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return ["CPUExecutionProvider"], [
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{
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"intra_op_num_threads": 8,
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"execution_mode": onnxruntime.ExecutionMode.ORT_PARALLEL,
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"inter_op_num_threads": 8,
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}
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]
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elif gpu >= 0 and "DmlExecutionProvider" in availableProviders:
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return ["DmlExecutionProvider"], [{"device_id": gpu}]
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else:
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return ["CPUExecutionProvider"], [
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{
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"intra_op_num_threads": 8,
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"execution_mode": onnxruntime.ExecutionMode.ORT_PARALLEL,
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"inter_op_num_threads": 8,
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}
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]
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def setForceTensor(self, forceTensor: bool):
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self.forceTensor = forceTensor
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def halfPrecisionAvailable(self, id: int):
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if self.gpu_num == 0:
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return False
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if id < 0:
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return False
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if self.forceTensor:
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return False
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try:
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gpuName = torch.cuda.get_device_name(id).upper()
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if (
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("16" in gpuName and "V100" not in gpuName)
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or "P40" in gpuName.upper()
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or "1070" in gpuName
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or "1080" in gpuName
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):
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return False
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except Exception as e:
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print(e)
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return False
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cap = torch.cuda.get_device_capability(id)
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if cap[0] < 7: # コンピューティング機能が7以上の場合half precisionが使えるとされている（が例外がある？T500とか）
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return False
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return True
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def getDeviceMemory(self, id: int):
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try:
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return torch.cuda.get_device_properties(id).total_memory
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except Exception as e:
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# except:
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print(e)
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return 0
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