386 lines
14 KiB
Python
386 lines
14 KiB
Python
|
import os,traceback
|
|||
|
|
import glob
|
|||
|
|
import sys
|
|||
|
|
import argparse
|
|||
|
|
import logging
|
|||
|
|
import json
|
|||
|
|
import subprocess
|
|||
|
|
import numpy as np
|
|||
|
|
from scipy.io.wavfile import read
|
|||
|
|
import torch
|
|||
|
|
|
|||
|
|
MATPLOTLIB_FLAG = False
|
|||
|
|
|
|||
|
|
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
|||
|
|
logger = logging
|
|||
|
|
|
|||
|
|
def load_checkpoint_d(checkpoint_path, combd,sbd, optimizer=None,load_opt=1):
|
|||
|
|
assert os.path.isfile(checkpoint_path)
|
|||
|
|
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
|||
|
|
|
|||
|
|
##################
|
|||
|
|
def go(model,bkey):
|
|||
|
|
saved_state_dict = checkpoint_dict[bkey]
|
|||
|
|
if hasattr(model, 'module'):state_dict = model.module.state_dict()
|
|||
|
|
else:state_dict = model.state_dict()
|
|||
|
|
new_state_dict= {}
|
|||
|
|
for k, v in state_dict.items():#模型需要的shape
|
|||
|
|
try:
|
|||
|
|
new_state_dict[k] = saved_state_dict[k]
|
|||
|
|
if(saved_state_dict[k].shape!=state_dict[k].shape):
|
|||
|
|
print("shape-%s-mismatch|need-%s|get-%s"%(k,state_dict[k].shape,saved_state_dict[k].shape))#
|
|||
|
|
raise KeyError
|
|||
|
|
except:
|
|||
|
|
# logger.info(traceback.format_exc())
|
|||
|
|
logger.info("%s is not in the checkpoint" % k)#pretrain缺失的
|
|||
|
|
new_state_dict[k] = v#模型自带的随机值
|
|||
|
|
if hasattr(model, 'module'):
|
|||
|
|
model.module.load_state_dict(new_state_dict,strict=False)
|
|||
|
|
else:
|
|||
|
|
model.load_state_dict(new_state_dict,strict=False)
|
|||
|
|
go(combd,"combd")
|
|||
|
|
go(sbd,"sbd")
|
|||
|
|
#############
|
|||
|
|
logger.info("Loaded model weights")
|
|||
|
|
|
|||
|
|
iteration = checkpoint_dict['iteration']
|
|||
|
|
learning_rate = checkpoint_dict['learning_rate']
|
|||
|
|
if optimizer is not None and load_opt==1:###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
|
|||
|
|
# try:
|
|||
|
|
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
|||
|
|
# except:
|
|||
|
|
# traceback.print_exc()
|
|||
|
|
logger.info("Loaded checkpoint '{}' (iteration {})" .format(checkpoint_path, iteration))
|
|||
|
|
return model, optimizer, learning_rate, iteration
|
|||
|
|
|
|||
|
|
|
|||
|
|
# def load_checkpoint(checkpoint_path, model, optimizer=None):
|
|||
|
|
# assert os.path.isfile(checkpoint_path)
|
|||
|
|
# checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
|||
|
|
# iteration = checkpoint_dict['iteration']
|
|||
|
|
# learning_rate = checkpoint_dict['learning_rate']
|
|||
|
|
# if optimizer is not None:
|
|||
|
|
# optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
|||
|
|
# # print(1111)
|
|||
|
|
# saved_state_dict = checkpoint_dict['model']
|
|||
|
|
# # print(1111)
|
|||
|
|
#
|
|||
|
|
# if hasattr(model, 'module'):
|
|||
|
|
# state_dict = model.module.state_dict()
|
|||
|
|
# else:
|
|||
|
|
# state_dict = model.state_dict()
|
|||
|
|
# new_state_dict= {}
|
|||
|
|
# for k, v in state_dict.items():
|
|||
|
|
# try:
|
|||
|
|
# new_state_dict[k] = saved_state_dict[k]
|
|||
|
|
# except:
|
|||
|
|
# logger.info("%s is not in the checkpoint" % k)
|
|||
|
|
# new_state_dict[k] = v
|
|||
|
|
# if hasattr(model, 'module'):
|
|||
|
|
# model.module.load_state_dict(new_state_dict)
|
|||
|
|
# else:
|
|||
|
|
# model.load_state_dict(new_state_dict)
|
|||
|
|
# logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
|||
|
|
# checkpoint_path, iteration))
|
|||
|
|
# return model, optimizer, learning_rate, iteration
|
|||
|
|
def load_checkpoint(checkpoint_path, model, optimizer=None,load_opt=1):
|
|||
|
|
assert os.path.isfile(checkpoint_path)
|
|||
|
|
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
|||
|
|
|
|||
|
|
saved_state_dict = checkpoint_dict['model']
|
|||
|
|
if hasattr(model, 'module'):
|
|||
|
|
state_dict = model.module.state_dict()
|
|||
|
|
else:
|
|||
|
|
state_dict = model.state_dict()
|
|||
|
|
new_state_dict= {}
|
|||
|
|
for k, v in state_dict.items():#模型需要的shape
|
|||
|
|
try:
|
|||
|
|
new_state_dict[k] = saved_state_dict[k]
|
|||
|
|
if(saved_state_dict[k].shape!=state_dict[k].shape):
|
|||
|
|
print("shape-%s-mismatch|need-%s|get-%s"%(k,state_dict[k].shape,saved_state_dict[k].shape))#
|
|||
|
|
raise KeyError
|
|||
|
|
except:
|
|||
|
|
# logger.info(traceback.format_exc())
|
|||
|
|
logger.info("%s is not in the checkpoint" % k)#pretrain缺失的
|
|||
|
|
new_state_dict[k] = v#模型自带的随机值
|
|||
|
|
if hasattr(model, 'module'):
|
|||
|
|
model.module.load_state_dict(new_state_dict,strict=False)
|
|||
|
|
else:
|
|||
|
|
model.load_state_dict(new_state_dict,strict=False)
|
|||
|
|
logger.info("Loaded model weights")
|
|||
|
|
|
|||
|
|
iteration = checkpoint_dict['iteration']
|
|||
|
|
learning_rate = checkpoint_dict['learning_rate']
|
|||
|
|
if optimizer is not None and load_opt==1:###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
|
|||
|
|
# try:
|
|||
|
|
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
|||
|
|
# except:
|
|||
|
|
# traceback.print_exc()
|
|||
|
|
logger.info("Loaded checkpoint '{}' (iteration {})" .format(checkpoint_path, iteration))
|
|||
|
|
return model, optimizer, learning_rate, iteration
|
|||
|
|
|
|||
|
|
|
|||
|
|
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
|||
|
|
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
|||
|
|
iteration, checkpoint_path))
|
|||
|
|
if hasattr(model, 'module'):
|
|||
|
|
state_dict = model.module.state_dict()
|
|||
|
|
else:
|
|||
|
|
state_dict = model.state_dict()
|
|||
|
|
torch.save({'model': state_dict,
|
|||
|
|
'iteration': iteration,
|
|||
|
|
'optimizer': optimizer.state_dict(),
|
|||
|
|
'learning_rate': learning_rate}, checkpoint_path)
|
|||
|
|
def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
|
|||
|
|
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
|||
|
|
iteration, checkpoint_path))
|
|||
|
|
if hasattr(combd, 'module'): state_dict_combd = combd.module.state_dict()
|
|||
|
|
else:state_dict_combd = combd.state_dict()
|
|||
|
|
if hasattr(sbd, 'module'): state_dict_sbd = sbd.module.state_dict()
|
|||
|
|
else:state_dict_sbd = sbd.state_dict()
|
|||
|
|
torch.save({
|
|||
|
|
'combd': state_dict_combd,
|
|||
|
|
'sbd': state_dict_sbd,
|
|||
|
|
'iteration': iteration,
|
|||
|
|
'optimizer': optimizer.state_dict(),
|
|||
|
|
'learning_rate': learning_rate}, checkpoint_path)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
|||
|
|
for k, v in scalars.items():
|
|||
|
|
writer.add_scalar(k, v, global_step)
|
|||
|
|
for k, v in histograms.items():
|
|||
|
|
writer.add_histogram(k, v, global_step)
|
|||
|
|
for k, v in images.items():
|
|||
|
|
writer.add_image(k, v, global_step, dataformats='HWC')
|
|||
|
|
for k, v in audios.items():
|
|||
|
|
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
|||
|
|
f_list = glob.glob(os.path.join(dir_path, regex))
|
|||
|
|
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
|||
|
|
x = f_list[-1]
|
|||
|
|
print(x)
|
|||
|
|
return x
|
|||
|
|
|
|||
|
|
|
|||
|
|
def plot_spectrogram_to_numpy(spectrogram):
|
|||
|
|
global MATPLOTLIB_FLAG
|
|||
|
|
if not MATPLOTLIB_FLAG:
|
|||
|
|
import matplotlib
|
|||
|
|
matplotlib.use("Agg")
|
|||
|
|
MATPLOTLIB_FLAG = True
|
|||
|
|
mpl_logger = logging.getLogger('matplotlib')
|
|||
|
|
mpl_logger.setLevel(logging.WARNING)
|
|||
|
|
import matplotlib.pylab as plt
|
|||
|
|
import numpy as np
|
|||
|
|
|
|||
|
|
fig, ax = plt.subplots(figsize=(10,2))
|
|||
|
|
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
|||
|
|
interpolation='none')
|
|||
|
|
plt.colorbar(im, ax=ax)
|
|||
|
|
plt.xlabel("Frames")
|
|||
|
|
plt.ylabel("Channels")
|
|||
|
|
plt.tight_layout()
|
|||
|
|
|
|||
|
|
fig.canvas.draw()
|
|||
|
|
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
|||
|
|
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
|||
|
|
plt.close()
|
|||
|
|
return data
|
|||
|
|
|
|||
|
|
|
|||
|
|
def plot_alignment_to_numpy(alignment, info=None):
|
|||
|
|
global MATPLOTLIB_FLAG
|
|||
|
|
if not MATPLOTLIB_FLAG:
|
|||
|
|
import matplotlib
|
|||
|
|
matplotlib.use("Agg")
|
|||
|
|
MATPLOTLIB_FLAG = True
|
|||
|
|
mpl_logger = logging.getLogger('matplotlib')
|
|||
|
|
mpl_logger.setLevel(logging.WARNING)
|
|||
|
|
import matplotlib.pylab as plt
|
|||
|
|
import numpy as np
|
|||
|
|
|
|||
|
|
fig, ax = plt.subplots(figsize=(6, 4))
|
|||
|
|
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
|||
|
|
interpolation='none')
|
|||
|
|
fig.colorbar(im, ax=ax)
|
|||
|
|
xlabel = 'Decoder timestep'
|
|||
|
|
if info is not None:
|
|||
|
|
xlabel += '\n\n' + info
|
|||
|
|
plt.xlabel(xlabel)
|
|||
|
|
plt.ylabel('Encoder timestep')
|
|||
|
|
plt.tight_layout()
|
|||
|
|
|
|||
|
|
fig.canvas.draw()
|
|||
|
|
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
|||
|
|
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
|||
|
|
plt.close()
|
|||
|
|
return data
|
|||
|
|
|
|||
|
|
|
|||
|
|
def load_wav_to_torch(full_path):
|
|||
|
|
sampling_rate, data = read(full_path)
|
|||
|
|
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
|||
|
|
|
|||
|
|
|
|||
|
|
def load_filepaths_and_text(filename, split="|"):
|
|||
|
|
with open(filename, encoding='utf-8') as f:
|
|||
|
|
filepaths_and_text = [line.strip().split(split) for line in f]
|
|||
|
|
return filepaths_and_text
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_hparams(init=True):
|
|||
|
|
'''
|
|||
|
|
todo:
|
|||
|
|
结尾七人组：
|
|||
|
|
保存频率、总epoch done
|
|||
|
|
bs done
|
|||
|
|
pretrainG、pretrainD done
|
|||
|
|
卡号：os.en["CUDA_VISIBLE_DEVICES"] done
|
|||
|
|
if_latest todo
|
|||
|
|
模型：if_f0 todo
|
|||
|
|
采样率：自动选择config done
|
|||
|
|
是否缓存数据集进GPU:if_cache_data_in_gpu done
|
|||
|
|
|
|||
|
|
-m:
|
|||
|
|
自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
|
|||
|
|
-c不要了
|
|||
|
|
'''
|
|||
|
|
parser = argparse.ArgumentParser()
|
|||
|
|
# parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
|
|||
|
|
parser.add_argument('-se', '--save_every_epoch', type=int, required=True,help='checkpoint save frequency (epoch)')
|
|||
|
|
parser.add_argument('-te', '--total_epoch', type=int, required=True,help='total_epoch')
|
|||
|
|
parser.add_argument('-pg', '--pretrainG', type=str, default="",help='Pretrained Discriminator path')
|
|||
|
|
parser.add_argument('-pd', '--pretrainD', type=str, default="",help='Pretrained Generator path')
|
|||
|
|
parser.add_argument('-g', '--gpus', type=str, default="0",help='split by -')
|
|||
|
|
parser.add_argument('-bs', '--batch_size', type=int, required=True,help='batch size')
|
|||
|
|
parser.add_argument('-e', '--experiment_dir', type=str, required=True,help='experiment dir')#-m
|
|||
|
|
parser.add_argument('-sr', '--sample_rate', type=str, required=True,help='sample rate, 32k/40k/48k')
|
|||
|
|
parser.add_argument('-f0', '--if_f0', type=int, required=True,help='use f0 as one of the inputs of the model, 1 or 0')
|
|||
|
|
parser.add_argument('-l', '--if_latest', type=int, required=True,help='if only save the latest G/D pth file, 1 or 0')
|
|||
|
|
parser.add_argument('-c', '--if_cache_data_in_gpu', type=int, required=True,help='if caching the dataset in GPU memory, 1 or 0')
|
|||
|
|
|
|||
|
|
args = parser.parse_args()
|
|||
|
|
name = args.experiment_dir
|
|||
|
|
experiment_dir = os.path.join("./logs", args.experiment_dir)
|
|||
|
|
|
|||
|
|
if not os.path.exists(experiment_dir):
|
|||
|
|
os.makedirs(experiment_dir)
|
|||
|
|
|
|||
|
|
config_path = "configs/%s.json"%args.sample_rate
|
|||
|
|
config_save_path = os.path.join(experiment_dir, "config.json")
|
|||
|
|
if init:
|
|||
|
|
with open(config_path, "r") as f:
|
|||
|
|
data = f.read()
|
|||
|
|
with open(config_save_path, "w") as f:
|
|||
|
|
f.write(data)
|
|||
|
|
else:
|
|||
|
|
with open(config_save_path, "r") as f:
|
|||
|
|
data = f.read()
|
|||
|
|
config = json.loads(data)
|
|||
|
|
|
|||
|
|
hparams = HParams(**config)
|
|||
|
|
hparams.model_dir = hparams.experiment_dir = experiment_dir
|
|||
|
|
hparams.save_every_epoch = args.save_every_epoch
|
|||
|
|
hparams.name = name
|
|||
|
|
hparams.total_epoch = args.total_epoch
|
|||
|
|
hparams.pretrainG = args.pretrainG
|
|||
|
|
hparams.pretrainD = args.pretrainD
|
|||
|
|
hparams.gpus = args.gpus
|
|||
|
|
hparams.train.batch_size = args.batch_size
|
|||
|
|
hparams.sample_rate = args.sample_rate
|
|||
|
|
hparams.if_f0 = args.if_f0
|
|||
|
|
hparams.if_latest = args.if_latest
|
|||
|
|
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
|
|||
|
|
hparams.data.training_files = "%s/filelist.txt"%experiment_dir
|
|||
|
|
return hparams
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_hparams_from_dir(model_dir):
|
|||
|
|
config_save_path = os.path.join(model_dir, "config.json")
|
|||
|
|
with open(config_save_path, "r") as f:
|
|||
|
|
data = f.read()
|
|||
|
|
config = json.loads(data)
|
|||
|
|
|
|||
|
|
hparams =HParams(**config)
|
|||
|
|
hparams.model_dir = model_dir
|
|||
|
|
return hparams
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_hparams_from_file(config_path):
|
|||
|
|
with open(config_path, "r") as f:
|
|||
|
|
data = f.read()
|
|||
|
|
config = json.loads(data)
|
|||
|
|
|
|||
|
|
hparams =HParams(**config)
|
|||
|
|
return hparams
|
|||
|
|
|
|||
|
|
|
|||
|
|
def check_git_hash(model_dir):
|
|||
|
|
source_dir = os.path.dirname(os.path.realpath(__file__))
|
|||
|
|
if not os.path.exists(os.path.join(source_dir, ".git")):
|
|||
|
|
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
|||
|
|
source_dir
|
|||
|
|
))
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
|||
|
|
|
|||
|
|
path = os.path.join(model_dir, "githash")
|
|||
|
|
if os.path.exists(path):
|
|||
|
|
saved_hash = open(path).read()
|
|||
|
|
if saved_hash != cur_hash:
|
|||
|
|
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
|||
|
|
saved_hash[:8], cur_hash[:8]))
|
|||
|
|
else:
|
|||
|
|
open(path, "w").write(cur_hash)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_logger(model_dir, filename="train.log"):
|
|||
|
|
global logger
|
|||
|
|
logger = logging.getLogger(os.path.basename(model_dir))
|
|||
|
|
logger.setLevel(logging.DEBUG)
|
|||
|
|
|
|||
|
|
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
|||
|
|
if not os.path.exists(model_dir):
|
|||
|
|
os.makedirs(model_dir)
|
|||
|
|
h = logging.FileHandler(os.path.join(model_dir, filename))
|
|||
|
|
h.setLevel(logging.DEBUG)
|
|||
|
|
h.setFormatter(formatter)
|
|||
|
|
logger.addHandler(h)
|
|||
|
|
return logger
|
|||
|
|
|
|||
|
|
|
|||
|
|
class HParams():
|
|||
|
|
def __init__(self, **kwargs):
|
|||
|
|
for k, v in kwargs.items():
|
|||
|
|
if type(v) == dict:
|
|||
|
|
v = HParams(**v)
|
|||
|
|
self[k] = v
|
|||
|
|
|
|||
|
|
def keys(self):
|
|||
|
|
return self.__dict__.keys()
|
|||
|
|
|
|||
|
|
def items(self):
|
|||
|
|
return self.__dict__.items()
|
|||
|
|
|
|||
|
|
def values(self):
|
|||
|
|
return self.__dict__.values()
|
|||
|
|
|
|||
|
|
def __len__(self):
|
|||
|
|
return len(self.__dict__)
|
|||
|
|
|
|||
|
|
def __getitem__(self, key):
|
|||
|
|
return getattr(self, key)
|
|||
|
|
|
|||
|
|
def __setitem__(self, key, value):
|
|||
|
|
return setattr(self, key, value)
|
|||
|
|
|
|||
|
|
def __contains__(self, key):
|
|||
|
|
return key in self.__dict__
|
|||
|
|
|
|||
|
|
def __repr__(self):
|
|||
|
|
return self.__dict__.__repr__()
|