| 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 |
|
|
| if torch.xpu.is_available(): |
| from infer.modules.ipex import ipex_init |
|
|
| ipex_init() |
| except Exception: |
| 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.python_cmd, |
| self.listen_port, |
| self.iscolab, |
| self.noparallel, |
| self.noautoopen, |
| self.dml, |
| self.nocheck, |
| ) = self.arg_parse() |
| 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 |
|
|
| @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( |
| "--dml", |
| action="store_true", |
| help="torch_dml", |
| ) |
| parser.add_argument( |
| "--nocheck", action="store_true", help="Run without checking assets" |
| ) |
| 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.dml, |
| cmd_opts.nocheck, |
| ) |
|
|
| |
| |
| @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 |
|
|
| @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: |
| |
| x_pad = 3 |
| x_query = 10 |
| x_center = 60 |
| x_max = 65 |
| else: |
| |
| 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 |
| if self.dml: |
| logger.info("Use DirectML instead") |
| if ( |
| os.path.exists( |
| "runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll" |
| ) |
| == False |
| ): |
| try: |
| os.rename( |
| "runtime\Lib\site-packages\onnxruntime", |
| "runtime\Lib\site-packages\onnxruntime-cuda", |
| ) |
| except: |
| pass |
| try: |
| os.rename( |
| "runtime\Lib\site-packages\onnxruntime-dml", |
| "runtime\Lib\site-packages\onnxruntime", |
| ) |
| except: |
| pass |
| |
| import torch_directml |
|
|
| self.device = torch_directml.device(torch_directml.default_device()) |
| self.is_half = False |
| 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 |
|
|
