Delete config.py

config.py

@@ -1,184 +0,0 @@
-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}")
-
-            strr = None
-        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}")
-
-            strr = None
-    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