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import datetime |
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import importlib |
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import logging |
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import os |
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import re |
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import subprocess |
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import sys |
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from pathlib import Path |
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from typing import Dict |
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import fsspec |
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import torch |
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def to_cuda(x: torch.Tensor) -> torch.Tensor: |
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if x is None: |
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return None |
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if torch.is_tensor(x): |
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x = x.contiguous() |
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if torch.cuda.is_available(): |
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x = x.cuda(non_blocking=True) |
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return x |
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def get_cuda(): |
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use_cuda = torch.cuda.is_available() |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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return use_cuda, device |
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def get_git_branch(): |
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try: |
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out = subprocess.check_output(["git", "branch"]).decode("utf8") |
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current = next(line for line in out.split("\n") if line.startswith("*")) |
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current.replace("* ", "") |
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except subprocess.CalledProcessError: |
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current = "inside_docker" |
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except FileNotFoundError: |
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current = "unknown" |
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except StopIteration: |
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current = "unknown" |
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return current |
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def get_commit_hash(): |
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"""https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script""" |
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try: |
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commit = subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode().strip() |
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except (subprocess.CalledProcessError, FileNotFoundError): |
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commit = "0000000" |
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return commit |
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def get_experiment_folder_path(root_path, model_name): |
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"""Get an experiment folder path with the current date and time""" |
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date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p") |
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commit_hash = get_commit_hash() |
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output_folder = os.path.join(root_path, model_name + "-" + date_str + "-" + commit_hash) |
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return output_folder |
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def remove_experiment_folder(experiment_path): |
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"""Check folder if there is a checkpoint, otherwise remove the folder""" |
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fs = fsspec.get_mapper(experiment_path).fs |
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checkpoint_files = fs.glob(experiment_path + "/*.pth") |
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if not checkpoint_files: |
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if fs.exists(experiment_path): |
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fs.rm(experiment_path, recursive=True) |
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print(" ! Run is removed from {}".format(experiment_path)) |
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else: |
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print(" ! Run is kept in {}".format(experiment_path)) |
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def count_parameters(model): |
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r"""Count number of trainable parameters in a network""" |
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return sum(p.numel() for p in model.parameters() if p.requires_grad) |
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def to_camel(text): |
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text = text.capitalize() |
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text = re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text) |
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text = text.replace("Tts", "TTS") |
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text = text.replace("vc", "VC") |
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return text |
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def find_module(module_path: str, module_name: str) -> object: |
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module_name = module_name.lower() |
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module = importlib.import_module(module_path + "." + module_name) |
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class_name = to_camel(module_name) |
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return getattr(module, class_name) |
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def import_class(module_path: str) -> object: |
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"""Import a class from a module path. |
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Args: |
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module_path (str): The module path of the class. |
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Returns: |
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object: The imported class. |
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""" |
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class_name = module_path.split(".")[-1] |
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module_path = ".".join(module_path.split(".")[:-1]) |
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module = importlib.import_module(module_path) |
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return getattr(module, class_name) |
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def get_import_path(obj: object) -> str: |
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"""Get the import path of a class. |
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Args: |
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obj (object): The class object. |
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Returns: |
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str: The import path of the class. |
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""" |
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return ".".join([type(obj).__module__, type(obj).__name__]) |
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def get_user_data_dir(appname): |
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TTS_HOME = os.environ.get("TTS_HOME") |
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XDG_DATA_HOME = os.environ.get("XDG_DATA_HOME") |
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if TTS_HOME is not None: |
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ans = Path(TTS_HOME).expanduser().resolve(strict=False) |
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elif XDG_DATA_HOME is not None: |
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ans = Path(XDG_DATA_HOME).expanduser().resolve(strict=False) |
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elif sys.platform == "win32": |
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import winreg |
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key = winreg.OpenKey( |
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winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders" |
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) |
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dir_, _ = winreg.QueryValueEx(key, "Local AppData") |
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ans = Path(dir_).resolve(strict=False) |
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elif sys.platform == "darwin": |
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ans = Path("~/Library/Application Support/").expanduser() |
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else: |
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ans = Path.home().joinpath(".local/share") |
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return ans.joinpath(appname) |
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def set_init_dict(model_dict, checkpoint_state, c): |
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for k, v in checkpoint_state.items(): |
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if k not in model_dict: |
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print(" | > Layer missing in the model definition: {}".format(k)) |
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pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict} |
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()} |
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if c.has("reinit_layers") and c.reinit_layers is not None: |
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for reinit_layer_name in c.reinit_layers: |
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k} |
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model_dict.update(pretrained_dict) |
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print(" | > {} / {} layers are restored.".format(len(pretrained_dict), len(model_dict))) |
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return model_dict |
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def format_aux_input(def_args: Dict, kwargs: Dict) -> Dict: |
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"""Format kwargs to hande auxilary inputs to models. |
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Args: |
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def_args (Dict): A dictionary of argument names and their default values if not defined in `kwargs`. |
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kwargs (Dict): A `dict` or `kwargs` that includes auxilary inputs to the model. |
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Returns: |
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Dict: arguments with formatted auxilary inputs. |
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""" |
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kwargs = kwargs.copy() |
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for name in def_args: |
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if name not in kwargs or kwargs[name] is None: |
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kwargs[name] = def_args[name] |
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return kwargs |
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class KeepAverage: |
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def __init__(self): |
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self.avg_values = {} |
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self.iters = {} |
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def __getitem__(self, key): |
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return self.avg_values[key] |
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def items(self): |
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return self.avg_values.items() |
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def add_value(self, name, init_val=0, init_iter=0): |
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self.avg_values[name] = init_val |
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self.iters[name] = init_iter |
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def update_value(self, name, value, weighted_avg=False): |
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if name not in self.avg_values: |
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self.add_value(name, init_val=value) |
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else: |
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if weighted_avg: |
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self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value |
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self.iters[name] += 1 |
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else: |
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self.avg_values[name] = self.avg_values[name] * self.iters[name] + value |
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self.iters[name] += 1 |
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self.avg_values[name] /= self.iters[name] |
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def add_values(self, name_dict): |
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for key, value in name_dict.items(): |
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self.add_value(key, init_val=value) |
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def update_values(self, value_dict): |
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for key, value in value_dict.items(): |
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self.update_value(key, value) |
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def get_timestamp(): |
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return datetime.now().strftime("%y%m%d-%H%M%S") |
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def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False): |
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lg = logging.getLogger(logger_name) |
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formatter = logging.Formatter("%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s", datefmt="%y-%m-%d %H:%M:%S") |
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lg.setLevel(level) |
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if tofile: |
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log_file = os.path.join(root, phase + "_{}.log".format(get_timestamp())) |
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fh = logging.FileHandler(log_file, mode="w") |
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fh.setFormatter(formatter) |
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lg.addHandler(fh) |
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if screen: |
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sh = logging.StreamHandler() |
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sh.setFormatter(formatter) |
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lg.addHandler(sh) |
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