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import argparse
import sys
import torch
import json
from multiprocessing import cpu_count
global usefp16
usefp16 = False
def use_fp32_config():
usefp16 = False
device_capability = 0
if torch.cuda.is_available():
device = torch.device("cuda:0") # Assuming you have only one GPU (index 0).
device_capability = torch.cuda.get_device_capability(device)[0]
if device_capability >= 7:
usefp16 = True
for config_file in ["32k.json", "40k.json", "48k.json"]:
with open(f"configs/{config_file}", "r") as d:
data = json.load(d)
if "train" in data and "fp16_run" in data["train"]:
data["train"]["fp16_run"] = True
with open(f"configs/{config_file}", "w") as d:
json.dump(data, d, indent=4)
print(f"Set fp16_run to true in {config_file}")
with open(
"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
) as f:
strr = f.read()
strr = strr.replace("3.0", "3.7")
with open(
"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
) as f:
f.write(strr)
else:
for config_file in ["32k.json", "40k.json", "48k.json"]:
with open(f"configs/{config_file}", "r") as f:
data = json.load(f)
if "train" in data and "fp16_run" in data["train"]:
data["train"]["fp16_run"] = False
with open(f"configs/{config_file}", "w") as d:
json.dump(data, d, indent=4)
print(f"Set fp16_run to false in {config_file}")
with open(
"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8"
) as f:
strr = f.read()
strr = strr.replace("3.7", "3.0")
with open(
"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8"
) as f:
f.write(strr)
else:
print(
"CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed."
)
return (usefp16, device_capability)
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.paperspace,
self.is_cli,
) = 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",
)
parser.add_argument( # Fork Feature. Paperspace integration for web UI
"--paperspace",
action="store_true",
help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.",
)
parser.add_argument( # Fork Feature. Embed a CLI into the infer-web.py
"--is_cli",
action="store_true",
help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!",
)
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,
cmd_opts.paperspace,
cmd_opts.is_cli,
)
# 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
else:
print("Found GPU", self.gpu_name)
use_fp32_config()
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
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