136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
import argparse
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import torch
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from multiprocessing import cpu_count
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def use_fp32_config():
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for config_file in ["32k.json", "40k.json", "48k.json"]:
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with open(f"configs/{config_file}", "r") as f:
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strr = f.read().replace("true", "false")
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with open(f"configs/{config_file}", "w") as f:
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f.write(strr)
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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class Config:
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def __init__(self):
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self.device = "cuda:0"
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self.is_half = True
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self.n_cpu = 0
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self.gpu_name = None
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self.gpu_mem = None
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(
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self.python_cmd,
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self.listen_port,
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self.iscolab,
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self.noparallel,
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self.noautoopen,
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) = self.arg_parse()
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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@staticmethod
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def arg_parse() -> tuple:
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=7865, help="Listen port")
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parser.add_argument(
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"--pycmd", type=str, default="python", help="Python command"
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)
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parser.add_argument("--colab", action="store_true", help="Launch in colab")
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parser.add_argument(
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"--noparallel", action="store_true", help="Disable parallel processing"
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)
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parser.add_argument(
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"--noautoopen",
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action="store_true",
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help="Do not open in browser automatically",
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)
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cmd_opts = parser.parse_args()
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cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
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return (
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cmd_opts.pycmd,
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cmd_opts.port,
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cmd_opts.colab,
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cmd_opts.noparallel,
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cmd_opts.noautoopen,
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)
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# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
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# check `getattr` and try it for compatibility
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@staticmethod
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def has_mps() -> bool:
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if not torch.backends.mps.is_available():
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return False
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try:
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torch.zeros(1).to(torch.device("mps"))
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return True
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except Exception:
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return False
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def device_config(self) -> tuple:
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if torch.cuda.is_available():
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i_device = int(self.device.split(":")[-1])
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self.gpu_name = torch.cuda.get_device_name(i_device)
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if (
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
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or "P40" in self.gpu_name.upper()
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or "1060" in self.gpu_name
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or "1070" in self.gpu_name
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or "1080" in self.gpu_name
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):
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print("Found GPU", self.gpu_name, ", force to fp32")
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self.is_half = False
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use_fp32_config()
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else:
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print("Found GPU", self.gpu_name)
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self.gpu_mem = int(
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torch.cuda.get_device_properties(i_device).total_memory
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/ 1024
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/ 1024
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/ 1024
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+ 0.4
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)
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if self.gpu_mem <= 4:
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with open("trainset_preprocess_pipeline_print.py", "r") as f:
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strr = f.read().replace("3.7", "3.0")
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with open("trainset_preprocess_pipeline_print.py", "w") as f:
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f.write(strr)
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elif self.has_mps():
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print("No supported Nvidia GPU found, use MPS instead")
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self.device = "mps"
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self.is_half = False
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use_fp32_config()
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else:
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print("No supported Nvidia GPU found, use CPU instead")
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self.device = "cpu"
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self.is_half = False
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use_fp32_config()
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if self.n_cpu == 0:
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self.n_cpu = cpu_count()
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if self.is_half:
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# 6G显存配置
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x_pad = 3
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x_query = 10
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x_center = 60
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x_max = 65
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else:
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# 5G显存配置
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x_pad = 1
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x_query = 6
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x_center = 38
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x_max = 41
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if self.gpu_mem != None and self.gpu_mem <= 4:
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x_pad = 1
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x_query = 5
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x_center = 30
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x_max = 32
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return x_pad, x_query, x_center, x_max
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