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import argparse | |
import torch | |
from multiprocessing import cpu_count | |
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.colab, | |
self.noparallel, | |
self.noautoopen, | |
self.api | |
) = self.arg_parse() | |
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() | |
def arg_parse() -> tuple: | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--port", type=int, default=7865, help="Listen port") | |
parser.add_argument( | |
"--pycmd", type=str, default="python", 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('--api', action="store_true", default=False) | |
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.api, | |
) | |
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("16系/10系显卡和P40强制单精度") | |
self.is_half = False | |
for config_file in ["32k.json", "40k.json", "48k.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) | |
else: | |
self.gpu_name = None | |
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 torch.backends.mps.is_available(): | |
print("没有发现支持的N卡, 使用MPS进行推理") | |
self.device = "mps" | |
self.is_half = False | |
else: | |
print("没有发现支持的N卡, 使用CPU进行推理") | |
self.device = "cpu" | |
self.is_half = False | |
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 |