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| import torch | |
| import json | |
| import os | |
| version_config_paths = [ | |
| os.path.join("48000.json"), | |
| os.path.join("40000.json"), | |
| os.path.join("44100.json"), | |
| os.path.join("32000.json"), | |
| ] | |
| def singleton(cls): | |
| instances = {} | |
| def get_instance(*args, **kwargs): | |
| if cls not in instances: | |
| instances[cls] = cls(*args, **kwargs) | |
| return instances[cls] | |
| return get_instance | |
| class Config: | |
| def __init__(self): | |
| self.device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| self.gpu_name = ( | |
| torch.cuda.get_device_name(int(self.device.split(":")[-1])) | |
| if self.device.startswith("cuda") | |
| else None | |
| ) | |
| self.json_config = self.load_config_json() | |
| self.gpu_mem = None | |
| self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() | |
| def load_config_json(self): | |
| configs = {} | |
| for config_file in version_config_paths: | |
| config_path = os.path.join("rvc", "configs", config_file) | |
| with open(config_path, "r") as f: | |
| configs[config_file] = json.load(f) | |
| return configs | |
| def device_config(self): | |
| if self.device.startswith("cuda"): | |
| self.set_cuda_config() | |
| else: | |
| self.device = "cpu" | |
| # Configuration for 6GB GPU memory | |
| x_pad, x_query, x_center, x_max = (1, 6, 38, 41) | |
| if self.gpu_mem is not None and self.gpu_mem <= 4: | |
| # Configuration for 5GB GPU memory | |
| x_pad, x_query, x_center, x_max = (1, 5, 30, 32) | |
| return x_pad, x_query, x_center, x_max | |
| def set_cuda_config(self): | |
| i_device = int(self.device.split(":")[-1]) | |
| self.gpu_name = torch.cuda.get_device_name(i_device) | |
| self.gpu_mem = torch.cuda.get_device_properties(i_device).total_memory // ( | |
| 1024**3 | |
| ) | |
| def max_vram_gpu(gpu): | |
| if torch.cuda.is_available(): | |
| gpu_properties = torch.cuda.get_device_properties(gpu) | |
| total_memory_gb = round(gpu_properties.total_memory / 1024 / 1024 / 1024) | |
| return total_memory_gb | |
| else: | |
| return "8" | |
| def get_gpu_info(): | |
| ngpu = torch.cuda.device_count() | |
| gpu_infos = [] | |
| if torch.cuda.is_available() or ngpu != 0: | |
| for i in range(ngpu): | |
| gpu_name = torch.cuda.get_device_name(i) | |
| mem = int( | |
| torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024 | |
| + 0.4 | |
| ) | |
| gpu_infos.append(f"{i}: {gpu_name} ({mem} GB)") | |
| if len(gpu_infos) > 0: | |
| gpu_info = "\n".join(gpu_infos) | |
| else: | |
| gpu_info = "Unfortunately, there is no compatible GPU available to support your training." | |
| return gpu_info | |
| def get_number_of_gpus(): | |
| if torch.cuda.is_available(): | |
| num_gpus = torch.cuda.device_count() | |
| return "-".join(map(str, range(num_gpus))) | |
| else: | |
| return "-" | |