Spaces:
Running
Running
File size: 6,896 Bytes
18bba3b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
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
|