142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
import argparse
|
|
import sys
|
|
import torch
|
|
from multiprocessing import cpu_count
|
|
|
|
|
|
def use_fp32_config():
|
|
for config_file in [
|
|
"32k.json",
|
|
"40k.json",
|
|
"48k.json",
|
|
"48k_v2.json",
|
|
"32k_v2.json",
|
|
]:
|
|
with open(f"configs/{config_file}", "r") as f:
|
|
strr = f.read().replace("true", "false")
|
|
with open(f"configs/{config_file}", "w") as f:
|
|
f.write(strr)
|
|
with open("trainset_preprocess_pipeline_print.py", "r") as f:
|
|
strr = f.read().replace("3.7", "3.0")
|
|
with open("trainset_preprocess_pipeline_print.py", "w") as f:
|
|
f.write(strr)
|
|
|
|
|
|
class Config:
|
|
def __init__(self):
|
|
self.device = "cuda:0"
|
|
self.is_half = True
|
|
self.n_cpu = 0
|
|
self.gpu_name = None
|
|
self.gpu_mem = None
|
|
(
|
|
self.python_cmd,
|
|
self.listen_port,
|
|
self.iscolab,
|
|
self.noparallel,
|
|
self.noautoopen,
|
|
) = self.arg_parse()
|
|
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
|
|
|
|
@staticmethod
|
|
def arg_parse() -> tuple:
|
|
exe = sys.executable or "python"
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--port", type=int, default=7865, help="Listen port")
|
|
parser.add_argument("--pycmd", type=str, default=exe, help="Python command")
|
|
parser.add_argument("--colab", action="store_true", help="Launch in colab")
|
|
parser.add_argument(
|
|
"--noparallel", action="store_true", help="Disable parallel processing"
|
|
)
|
|
parser.add_argument(
|
|
"--noautoopen",
|
|
action="store_true",
|
|
help="Do not open in browser automatically",
|
|
)
|
|
cmd_opts = parser.parse_args()
|
|
|
|
cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865
|
|
|
|
return (
|
|
cmd_opts.pycmd,
|
|
cmd_opts.port,
|
|
cmd_opts.colab,
|
|
cmd_opts.noparallel,
|
|
cmd_opts.noautoopen,
|
|
)
|
|
|
|
# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
|
|
# check `getattr` and try it for compatibility
|
|
@staticmethod
|
|
def has_mps() -> bool:
|
|
if not torch.backends.mps.is_available():
|
|
return False
|
|
try:
|
|
torch.zeros(1).to(torch.device("mps"))
|
|
return True
|
|
except Exception:
|
|
return False
|
|
|
|
def device_config(self) -> tuple:
|
|
if torch.cuda.is_available():
|
|
i_device = int(self.device.split(":")[-1])
|
|
self.gpu_name = torch.cuda.get_device_name(i_device)
|
|
if (
|
|
("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
|
|
or "P40" in self.gpu_name.upper()
|
|
or "1060" in self.gpu_name
|
|
or "1070" in self.gpu_name
|
|
or "1080" in self.gpu_name
|
|
):
|
|
print("Found GPU", self.gpu_name, ", force to fp32")
|
|
self.is_half = False
|
|
use_fp32_config()
|
|
else:
|
|
print("Found GPU", self.gpu_name)
|
|
self.gpu_mem = int(
|
|
torch.cuda.get_device_properties(i_device).total_memory
|
|
/ 1024
|
|
/ 1024
|
|
/ 1024
|
|
+ 0.4
|
|
)
|
|
if self.gpu_mem <= 4:
|
|
with open("trainset_preprocess_pipeline_print.py", "r") as f:
|
|
strr = f.read().replace("3.7", "3.0")
|
|
with open("trainset_preprocess_pipeline_print.py", "w") as f:
|
|
f.write(strr)
|
|
elif self.has_mps():
|
|
print("No supported Nvidia GPU found, use MPS instead")
|
|
self.device = "mps"
|
|
self.is_half = False
|
|
use_fp32_config()
|
|
else:
|
|
print("No supported Nvidia GPU found, use CPU instead")
|
|
self.device = "cpu"
|
|
self.is_half = False
|
|
use_fp32_config()
|
|
|
|
if self.n_cpu == 0:
|
|
self.n_cpu = cpu_count()
|
|
|
|
if self.is_half:
|
|
# 6G显存配置
|
|
x_pad = 3
|
|
x_query = 10
|
|
x_center = 60
|
|
x_max = 65
|
|
else:
|
|
# 5G显存配置
|
|
x_pad = 1
|
|
x_query = 6
|
|
x_center = 38
|
|
x_max = 41
|
|
|
|
if self.gpu_mem != None and self.gpu_mem <= 4:
|
|
x_pad = 1
|
|
x_query = 5
|
|
x_center = 30
|
|
x_max = 32
|
|
|
|
return x_pad, x_query, x_center, x_max
|