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) pass 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