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| 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}") | |
| 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}") | |
| 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.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() | |
| # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. | |
| # check `getattr` and try it for compatibility | |
| 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 | |
| ) | |
| 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 | |