Remove: remove currently unused code in utils.py
This commit is contained in:
tsukumi
286
utils.py
286
utils.py
@@ -8,28 +8,16 @@ import subprocess
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import numpy as np
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import torch
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from huggingface_hub import hf_hub_download
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from safetensors import safe_open
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from safetensors.torch import save_file
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from scipy.io.wavfile import read
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from style_bert_vits2.logging import logger
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MATPLOTLIB_FLAG = False
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def download_checkpoint(
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dir_path, repo_config, token=None, regex="G_*.pth", mirror="openi"
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):
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repo_id = repo_config["repo_id"]
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f_list = glob.glob(os.path.join(dir_path, regex))
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if f_list:
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print("Use existed model, skip downloading.")
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return
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for file in ["DUR_0.pth", "D_0.pth", "G_0.pth"]:
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hf_hub_download(repo_id, file, local_dir=dir_path, local_dir_use_symlinks=False)
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def load_checkpoint(
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checkpoint_path, model, optimizer=None, skip_optimizer=False, for_infer=False
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):
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@@ -114,28 +102,54 @@ def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path)
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)
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def save_safetensors(model, iteration, checkpoint_path, is_half=False, for_infer=False):
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"""
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Save model with safetensors.
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"""
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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keys = []
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for k in state_dict:
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if "enc_q" in k and for_infer:
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continue # noqa: E701
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keys.append(k)
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def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
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"""Freeing up space by deleting saved ckpts
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new_dict = (
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{k: state_dict[k].half() for k in keys}
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if is_half
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else {k: state_dict[k] for k in keys}
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Arguments:
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path_to_models -- Path to the model directory
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n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
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sort_by_time -- True -> chronologically delete ckpts
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False -> lexicographically delete ckpts
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"""
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import re
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ckpts_files = [
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f
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for f in os.listdir(path_to_models)
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if os.path.isfile(os.path.join(path_to_models, f))
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]
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def name_key(_f):
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return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
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def time_key(_f):
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return os.path.getmtime(os.path.join(path_to_models, _f))
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sort_key = time_key if sort_by_time else name_key
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def x_sorted(_x):
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return sorted(
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[f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
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key=sort_key,
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)
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new_dict["iteration"] = torch.LongTensor([iteration])
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logger.info(f"Saved safetensors to {checkpoint_path}")
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save_file(new_dict, checkpoint_path)
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to_del = [
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os.path.join(path_to_models, fn)
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for fn in (
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x_sorted("G_")[:-n_ckpts_to_keep]
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+ x_sorted("D_")[:-n_ckpts_to_keep]
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+ x_sorted("WD_")[:-n_ckpts_to_keep]
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+ x_sorted("DUR_")[:-n_ckpts_to_keep]
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)
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]
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def del_info(fn):
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return logger.info(f"Free up space by deleting ckpt {fn}")
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def del_routine(x):
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return [os.remove(x), del_info(x)]
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[del_routine(fn) for fn in to_del]
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def load_safetensors(checkpoint_path, model, for_infer=False):
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@@ -169,6 +183,30 @@ def load_safetensors(checkpoint_path, model, for_infer=False):
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return model, iteration
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def save_safetensors(model, iteration, checkpoint_path, is_half=False, for_infer=False):
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"""
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Save model with safetensors.
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"""
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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keys = []
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for k in state_dict:
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if "enc_q" in k and for_infer:
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continue # noqa: E701
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keys.append(k)
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new_dict = (
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{k: state_dict[k].half() for k in keys}
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if is_half
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else {k: state_dict[k] for k in keys}
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)
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new_dict["iteration"] = torch.LongTensor([iteration])
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logger.info(f"Saved safetensors to {checkpoint_path}")
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save_file(new_dict, checkpoint_path)
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def summarize(
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writer,
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global_step,
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@@ -274,6 +312,51 @@ def load_filepaths_and_text(filename, split="|"):
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return filepaths_and_text
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def get_logger(model_dir, filename="train.log"):
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global logger
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logger = logging.getLogger(os.path.basename(model_dir))
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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h = logging.FileHandler(os.path.join(model_dir, filename))
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h.setLevel(logging.DEBUG)
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h.setFormatter(formatter)
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logger.addHandler(h)
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return logger
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def get_steps(model_path):
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matches = re.findall(r"\d+", model_path)
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return matches[-1] if matches else None
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def check_git_hash(model_dir):
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source_dir = os.path.dirname(os.path.realpath(__file__))
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if not os.path.exists(os.path.join(source_dir, ".git")):
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logger.warning(
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"{} is not a git repository, therefore hash value comparison will be ignored.".format(
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source_dir
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)
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)
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return
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cur_hash = subprocess.getoutput("git rev-parse HEAD")
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path = os.path.join(model_dir, "githash")
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if os.path.exists(path):
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saved_hash = open(path).read()
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if saved_hash != cur_hash:
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logger.warning(
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"git hash values are different. {}(saved) != {}(current)".format(
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saved_hash[:8], cur_hash[:8]
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)
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)
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else:
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open(path, "w").write(cur_hash)
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def get_hparams(init=True):
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parser = argparse.ArgumentParser()
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parser.add_argument(
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@@ -307,67 +390,6 @@ def get_hparams(init=True):
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return hparams
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def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
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"""Freeing up space by deleting saved ckpts
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Arguments:
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path_to_models -- Path to the model directory
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n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
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sort_by_time -- True -> chronologically delete ckpts
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False -> lexicographically delete ckpts
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"""
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import re
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ckpts_files = [
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f
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for f in os.listdir(path_to_models)
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if os.path.isfile(os.path.join(path_to_models, f))
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]
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def name_key(_f):
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return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
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def time_key(_f):
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return os.path.getmtime(os.path.join(path_to_models, _f))
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sort_key = time_key if sort_by_time else name_key
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def x_sorted(_x):
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return sorted(
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[f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
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key=sort_key,
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)
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to_del = [
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os.path.join(path_to_models, fn)
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for fn in (
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x_sorted("G_")[:-n_ckpts_to_keep]
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+ x_sorted("D_")[:-n_ckpts_to_keep]
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+ x_sorted("WD_")[:-n_ckpts_to_keep]
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+ x_sorted("DUR_")[:-n_ckpts_to_keep]
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)
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]
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def del_info(fn):
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return logger.info(f"Free up space by deleting ckpt {fn}")
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def del_routine(x):
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return [os.remove(x), del_info(x)]
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[del_routine(fn) for fn in to_del]
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def get_hparams_from_dir(model_dir):
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config_save_path = os.path.join(model_dir, "config.json")
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with open(config_save_path, "r", encoding="utf-8") as f:
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data = f.read()
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config = json.loads(data)
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hparams = HParams(**config)
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hparams.model_dir = model_dir
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return hparams
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def get_hparams_from_file(config_path):
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# print("config_path: ", config_path)
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with open(config_path, "r", encoding="utf-8") as f:
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@@ -378,46 +400,6 @@ def get_hparams_from_file(config_path):
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return hparams
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def check_git_hash(model_dir):
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source_dir = os.path.dirname(os.path.realpath(__file__))
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if not os.path.exists(os.path.join(source_dir, ".git")):
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logger.warning(
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"{} is not a git repository, therefore hash value comparison will be ignored.".format(
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source_dir
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)
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)
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return
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cur_hash = subprocess.getoutput("git rev-parse HEAD")
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path = os.path.join(model_dir, "githash")
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if os.path.exists(path):
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saved_hash = open(path).read()
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if saved_hash != cur_hash:
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logger.warning(
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"git hash values are different. {}(saved) != {}(current)".format(
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saved_hash[:8], cur_hash[:8]
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)
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)
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else:
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open(path, "w").write(cur_hash)
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def get_logger(model_dir, filename="train.log"):
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global logger
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logger = logging.getLogger(os.path.basename(model_dir))
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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h = logging.FileHandler(os.path.join(model_dir, filename))
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h.setLevel(logging.DEBUG)
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h.setFormatter(formatter)
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logger.addHandler(h)
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return logger
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class HParams:
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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@@ -448,39 +430,3 @@ class HParams:
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def __repr__(self):
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return self.__dict__.__repr__()
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def mix_model(
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network1, network2, output_path, voice_ratio=(0.5, 0.5), tone_ratio=(0.5, 0.5)
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):
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if hasattr(network1, "module"):
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state_dict1 = network1.module.state_dict()
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state_dict2 = network2.module.state_dict()
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else:
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state_dict1 = network1.state_dict()
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state_dict2 = network2.state_dict()
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for k in state_dict1.keys():
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if k not in state_dict2.keys():
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continue
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if "enc_p" in k:
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state_dict1[k] = (
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state_dict1[k].clone() * tone_ratio[0]
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+ state_dict2[k].clone() * tone_ratio[1]
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)
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else:
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state_dict1[k] = (
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state_dict1[k].clone() * voice_ratio[0]
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+ state_dict2[k].clone() * voice_ratio[1]
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)
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for k in state_dict2.keys():
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if k not in state_dict1.keys():
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state_dict1[k] = state_dict2[k].clone()
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torch.save(
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{"model": state_dict1, "iteration": 0, "optimizer": None, "learning_rate": 0},
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output_path,
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)
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def get_steps(model_path):
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matches = re.findall(r"\d+", model_path)
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return matches[-1] if matches else None
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