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import argparse
import sys
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 = "python"
self.listen_port = 7865
self.iscolab = False
self.noparallel = False
self.noautoopen = False
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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
or "T4" in self.gpu_name.upper() # Add this line to check for T4 GPU
):
print("Found GPU", self.gpu_name, ", force to fp32")
self.is_half = False
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
)
elif self.has_mps():
print("No supported Nvidia GPU found, use MPS instead")
self.device = "mps"
self.is_half = False
else:
print("No supported Nvidia GPU found, use CPU instead")
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 is not 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
# has_mps is only available in nightly pytorch (for now) and macOS 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 |