import argparse import os import sys import json import shutil from multiprocessing import cpu_count import torch try: import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import if torch.xpu.is_available(): from infer.modules.ipex import ipex_init ipex_init() except Exception: # pylint: disable=broad-exception-caught pass import logging logger = logging.getLogger(__name__) version_config_list = [ "v1/32k.json", "v1/40k.json", "v1/48k.json", "v2/48k.json", "v2/32k.json", ] def singleton_variable(func): def wrapper(*args, **kwargs): if not wrapper.instance: wrapper.instance = func(*args, **kwargs) return wrapper.instance wrapper.instance = None return wrapper @singleton_variable class Config: def __init__(self): self.device = "cuda:0" self.is_half = True self.use_jit = False self.n_cpu = 0 self.gpu_name = None self.json_config = self.load_config_json() self.gpu_mem = None self.instead = "" self.preprocess_per = 3.7 self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() @staticmethod def load_config_json() -> dict: d = {} for config_file in version_config_list: p = f"configs/inuse/{config_file}" if not os.path.exists(p): shutil.copy(f"configs/{config_file}", p) with open(f"configs/inuse/{config_file}", "r") as f: d[config_file] = json.load(f) return d 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 @staticmethod def has_xpu() -> bool: if hasattr(torch, "xpu") and torch.xpu.is_available(): return True else: return False def use_fp32_config(self): for config_file in version_config_list: self.json_config[config_file]["train"]["fp16_run"] = False with open(f"configs/inuse/{config_file}", "r") as f: strr = f.read().replace("true", "false") with open(f"configs/inuse/{config_file}", "w") as f: f.write(strr) logger.info("overwrite " + config_file) self.preprocess_per = 3.0 logger.info("overwrite preprocess_per to %d" % (self.preprocess_per)) def device_config(self) -> tuple: if torch.cuda.is_available(): if self.has_xpu(): self.device = self.instead = "xpu:0" self.is_half = True 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 "P10" in self.gpu_name.upper() or "1060" in self.gpu_name or "1070" in self.gpu_name or "1080" in self.gpu_name ): logger.info("Found GPU %s, force to fp32", self.gpu_name) self.is_half = False self.use_fp32_config() else: logger.info("Found GPU %s", 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: self.preprocess_per = 3.0 elif self.has_mps(): logger.info("No supported Nvidia GPU found") self.device = self.instead = "mps" self.is_half = False self.use_fp32_config() else: logger.info("No supported Nvidia GPU found") self.device = self.instead = "cpu" self.is_half = False self.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 is not None and self.gpu_mem <= 4: x_pad = 1 x_query = 5 x_center = 30 x_max = 32 else: if self.instead: logger.info(f"Use {self.instead} instead") if ( os.path.exists( "runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll" ) == False ): try: os.rename( "runtime\Lib\site-packages\onnxruntime", "runtime\Lib\site-packages\onnxruntime-dml", ) except: pass try: os.rename( "runtime\Lib\site-packages\onnxruntime-cuda", "runtime\Lib\site-packages\onnxruntime", ) except: pass logger.info( "Half-precision floating-point: %s, device: %s" % (self.is_half, self.device) ) return x_pad, x_query, x_center, x_max