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# -*- coding: utf-8 -*- | |
import datetime | |
import importlib | |
import logging | |
import os | |
import re | |
import subprocess | |
import sys | |
from pathlib import Path | |
from typing import Dict | |
import fsspec | |
import torch | |
def to_cuda(x: torch.Tensor) -> torch.Tensor: | |
if x is None: | |
return None | |
if torch.is_tensor(x): | |
x = x.contiguous() | |
if torch.cuda.is_available(): | |
x = x.cuda(non_blocking=True) | |
return x | |
def get_cuda(): | |
use_cuda = torch.cuda.is_available() | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
return use_cuda, device | |
def get_git_branch(): | |
try: | |
out = subprocess.check_output(["git", "branch"]).decode("utf8") | |
current = next(line for line in out.split("\n") if line.startswith("*")) | |
current.replace("* ", "") | |
except subprocess.CalledProcessError: | |
current = "inside_docker" | |
except FileNotFoundError: | |
current = "unknown" | |
except StopIteration: | |
current = "unknown" | |
return current | |
def get_commit_hash(): | |
"""https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script""" | |
# try: | |
# subprocess.check_output(['git', 'diff-index', '--quiet', | |
# 'HEAD']) # Verify client is clean | |
# except: | |
# raise RuntimeError( | |
# " !! Commit before training to get the commit hash.") | |
try: | |
commit = subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode().strip() | |
# Not copying .git folder into docker container | |
except (subprocess.CalledProcessError, FileNotFoundError): | |
commit = "0000000" | |
return commit | |
def get_experiment_folder_path(root_path, model_name): | |
"""Get an experiment folder path with the current date and time""" | |
date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p") | |
commit_hash = get_commit_hash() | |
output_folder = os.path.join(root_path, model_name + "-" + date_str + "-" + commit_hash) | |
return output_folder | |
def remove_experiment_folder(experiment_path): | |
"""Check folder if there is a checkpoint, otherwise remove the folder""" | |
fs = fsspec.get_mapper(experiment_path).fs | |
checkpoint_files = fs.glob(experiment_path + "/*.pth") | |
if not checkpoint_files: | |
if fs.exists(experiment_path): | |
fs.rm(experiment_path, recursive=True) | |
print(" ! Run is removed from {}".format(experiment_path)) | |
else: | |
print(" ! Run is kept in {}".format(experiment_path)) | |
def count_parameters(model): | |
r"""Count number of trainable parameters in a network""" | |
return sum(p.numel() for p in model.parameters() if p.requires_grad) | |
def to_camel(text): | |
text = text.capitalize() | |
text = re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text) | |
text = text.replace("Tts", "TTS") | |
text = text.replace("vc", "VC") | |
return text | |
def find_module(module_path: str, module_name: str) -> object: | |
module_name = module_name.lower() | |
module = importlib.import_module(module_path + "." + module_name) | |
class_name = to_camel(module_name) | |
return getattr(module, class_name) | |
def import_class(module_path: str) -> object: | |
"""Import a class from a module path. | |
Args: | |
module_path (str): The module path of the class. | |
Returns: | |
object: The imported class. | |
""" | |
class_name = module_path.split(".")[-1] | |
module_path = ".".join(module_path.split(".")[:-1]) | |
module = importlib.import_module(module_path) | |
return getattr(module, class_name) | |
def get_import_path(obj: object) -> str: | |
"""Get the import path of a class. | |
Args: | |
obj (object): The class object. | |
Returns: | |
str: The import path of the class. | |
""" | |
return ".".join([type(obj).__module__, type(obj).__name__]) | |
def get_user_data_dir(appname): | |
TTS_HOME = os.environ.get("TTS_HOME") | |
XDG_DATA_HOME = os.environ.get("XDG_DATA_HOME") | |
if TTS_HOME is not None: | |
ans = Path(TTS_HOME).expanduser().resolve(strict=False) | |
elif XDG_DATA_HOME is not None: | |
ans = Path(XDG_DATA_HOME).expanduser().resolve(strict=False) | |
elif sys.platform == "win32": | |
import winreg # pylint: disable=import-outside-toplevel | |
key = winreg.OpenKey( | |
winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders" | |
) | |
dir_, _ = winreg.QueryValueEx(key, "Local AppData") | |
ans = Path(dir_).resolve(strict=False) | |
elif sys.platform == "darwin": | |
ans = Path("~/Library/Application Support/").expanduser() | |
else: | |
ans = Path.home().joinpath(".local/share") | |
return ans.joinpath(appname) | |
def set_init_dict(model_dict, checkpoint_state, c): | |
# Partial initialization: if there is a mismatch with new and old layer, it is skipped. | |
for k, v in checkpoint_state.items(): | |
if k not in model_dict: | |
print(" | > Layer missing in the model definition: {}".format(k)) | |
# 1. filter out unnecessary keys | |
pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict} | |
# 2. filter out different size layers | |
pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()} | |
# 3. skip reinit layers | |
if c.has("reinit_layers") and c.reinit_layers is not None: | |
for reinit_layer_name in c.reinit_layers: | |
pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k} | |
# 4. overwrite entries in the existing state dict | |
model_dict.update(pretrained_dict) | |
print(" | > {} / {} layers are restored.".format(len(pretrained_dict), len(model_dict))) | |
return model_dict | |
def format_aux_input(def_args: Dict, kwargs: Dict) -> Dict: | |
"""Format kwargs to hande auxilary inputs to models. | |
Args: | |
def_args (Dict): A dictionary of argument names and their default values if not defined in `kwargs`. | |
kwargs (Dict): A `dict` or `kwargs` that includes auxilary inputs to the model. | |
Returns: | |
Dict: arguments with formatted auxilary inputs. | |
""" | |
kwargs = kwargs.copy() | |
for name in def_args: | |
if name not in kwargs or kwargs[name] is None: | |
kwargs[name] = def_args[name] | |
return kwargs | |
class KeepAverage: | |
def __init__(self): | |
self.avg_values = {} | |
self.iters = {} | |
def __getitem__(self, key): | |
return self.avg_values[key] | |
def items(self): | |
return self.avg_values.items() | |
def add_value(self, name, init_val=0, init_iter=0): | |
self.avg_values[name] = init_val | |
self.iters[name] = init_iter | |
def update_value(self, name, value, weighted_avg=False): | |
if name not in self.avg_values: | |
# add value if not exist before | |
self.add_value(name, init_val=value) | |
else: | |
# else update existing value | |
if weighted_avg: | |
self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value | |
self.iters[name] += 1 | |
else: | |
self.avg_values[name] = self.avg_values[name] * self.iters[name] + value | |
self.iters[name] += 1 | |
self.avg_values[name] /= self.iters[name] | |
def add_values(self, name_dict): | |
for key, value in name_dict.items(): | |
self.add_value(key, init_val=value) | |
def update_values(self, value_dict): | |
for key, value in value_dict.items(): | |
self.update_value(key, value) | |
def get_timestamp(): | |
return datetime.now().strftime("%y%m%d-%H%M%S") | |
def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False): | |
lg = logging.getLogger(logger_name) | |
formatter = logging.Formatter("%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s", datefmt="%y-%m-%d %H:%M:%S") | |
lg.setLevel(level) | |
if tofile: | |
log_file = os.path.join(root, phase + "_{}.log".format(get_timestamp())) | |
fh = logging.FileHandler(log_file, mode="w") | |
fh.setFormatter(formatter) | |
lg.addHandler(fh) | |
if screen: | |
sh = logging.StreamHandler() | |
sh.setFormatter(formatter) | |
lg.addHandler(sh) | |