479 lines
15 KiB
Python
479 lines
15 KiB
Python
import argparse
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import glob
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import json
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import logging
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import os
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import subprocess
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import sys
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import shutil
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import numpy as np
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import torch
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from scipy.io.wavfile import read
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MATPLOTLIB_FLAG = False
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logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
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logger = logging
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def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
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##################
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def go(model, bkey):
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saved_state_dict = checkpoint_dict[bkey]
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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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new_state_dict = {}
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for k, v in state_dict.items(): # 模型需要的shape
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try:
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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logger.warning(
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"shape-%s-mismatch. need: %s, get: %s",
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k,
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state_dict[k].shape,
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saved_state_dict[k].shape,
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) #
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raise KeyError
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except:
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# logger.info(traceback.format_exc())
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logger.info("%s is not in the checkpoint", k) # pretrain缺失的
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new_state_dict[k] = v # 模型自带的随机值
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if hasattr(model, "module"):
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model.module.load_state_dict(new_state_dict, strict=False)
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else:
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model.load_state_dict(new_state_dict, strict=False)
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return model
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go(combd, "combd")
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model = go(sbd, "sbd")
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#############
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logger.info("Loaded model weights")
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iteration = checkpoint_dict["iteration"]
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learning_rate = checkpoint_dict["learning_rate"]
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if (
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optimizer is not None and load_opt == 1
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): ###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
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# try:
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optimizer.load_state_dict(checkpoint_dict["optimizer"])
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# except:
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# traceback.print_exc()
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logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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# def load_checkpoint(checkpoint_path, model, optimizer=None):
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# assert os.path.isfile(checkpoint_path)
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# checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
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# iteration = checkpoint_dict['iteration']
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# learning_rate = checkpoint_dict['learning_rate']
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# if optimizer is not None:
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# optimizer.load_state_dict(checkpoint_dict['optimizer'])
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# # print(1111)
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# saved_state_dict = checkpoint_dict['model']
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# # print(1111)
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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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# new_state_dict= {}
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# for k, v in state_dict.items():
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# try:
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# new_state_dict[k] = saved_state_dict[k]
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# except:
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# logger.info("%s is not in the checkpoint" % k)
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# new_state_dict[k] = v
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# if hasattr(model, 'module'):
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# model.module.load_state_dict(new_state_dict)
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# else:
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# model.load_state_dict(new_state_dict)
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# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
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# checkpoint_path, iteration))
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# return model, optimizer, learning_rate, iteration
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def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
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saved_state_dict = checkpoint_dict["model"]
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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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new_state_dict = {}
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for k, v in state_dict.items(): # 模型需要的shape
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try:
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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logger.warning(
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"shape-%s-mismatch|need-%s|get-%s",
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k,
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state_dict[k].shape,
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saved_state_dict[k].shape,
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) #
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raise KeyError
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except:
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# logger.info(traceback.format_exc())
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logger.info("%s is not in the checkpoint", k) # pretrain缺失的
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new_state_dict[k] = v # 模型自带的随机值
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if hasattr(model, "module"):
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model.module.load_state_dict(new_state_dict, strict=False)
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else:
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model.load_state_dict(new_state_dict, strict=False)
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logger.info("Loaded model weights")
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iteration = checkpoint_dict["iteration"]
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learning_rate = checkpoint_dict["learning_rate"]
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if (
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optimizer is not None and load_opt == 1
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): ###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
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# try:
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optimizer.load_state_dict(checkpoint_dict["optimizer"])
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# except:
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# traceback.print_exc()
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logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info(
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"Saving model and optimizer state at epoch {} to {}".format(
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iteration, checkpoint_path
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)
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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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torch.save(
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{
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"model": state_dict,
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"iteration": iteration,
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"optimizer": optimizer.state_dict(),
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"learning_rate": learning_rate,
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},
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checkpoint_path,
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)
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def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info(
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"Saving model and optimizer state at epoch {} to {}".format(
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iteration, checkpoint_path
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)
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)
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if hasattr(combd, "module"):
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state_dict_combd = combd.module.state_dict()
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else:
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state_dict_combd = combd.state_dict()
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if hasattr(sbd, "module"):
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state_dict_sbd = sbd.module.state_dict()
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else:
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state_dict_sbd = sbd.state_dict()
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torch.save(
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{
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"combd": state_dict_combd,
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"sbd": state_dict_sbd,
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"iteration": iteration,
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"optimizer": optimizer.state_dict(),
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"learning_rate": learning_rate,
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},
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checkpoint_path,
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)
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def summarize(
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writer,
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global_step,
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scalars={},
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histograms={},
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images={},
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audios={},
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audio_sampling_rate=22050,
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):
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for k, v in scalars.items():
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writer.add_scalar(k, v, global_step)
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for k, v in histograms.items():
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writer.add_histogram(k, v, global_step)
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for k, v in images.items():
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writer.add_image(k, v, global_step, dataformats="HWC")
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for k, v in audios.items():
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writer.add_audio(k, v, global_step, audio_sampling_rate)
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def latest_checkpoint_path(dir_path, regex="G_*.pth"):
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
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x = f_list[-1]
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logger.debug(x)
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return x
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def plot_spectrogram_to_numpy(spectrogram):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger("matplotlib")
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(10, 2))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
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plt.colorbar(im, ax=ax)
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plt.xlabel("Frames")
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plt.ylabel("Channels")
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def plot_alignment_to_numpy(alignment, info=None):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger("matplotlib")
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.imshow(
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alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
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)
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fig.colorbar(im, ax=ax)
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xlabel = "Decoder timestep"
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if info is not None:
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xlabel += "\n\n" + info
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plt.xlabel(xlabel)
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plt.ylabel("Encoder timestep")
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def load_wav_to_torch(full_path):
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sampling_rate, data = read(full_path)
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return torch.FloatTensor(data.astype(np.float32)), sampling_rate
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def load_filepaths_and_text(filename, split="|"):
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with open(filename, encoding="utf-8") as f:
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filepaths_and_text = [line.strip().split(split) for line in f]
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return filepaths_and_text
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def get_hparams(init=True):
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"""
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todo:
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结尾七人组：
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保存频率、总epoch done
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bs done
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pretrainG、pretrainD done
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卡号：os.en["CUDA_VISIBLE_DEVICES"] done
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if_latest done
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模型：if_f0 done
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采样率：自动选择config done
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是否缓存数据集进GPU:if_cache_data_in_gpu done
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-m:
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自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
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-c不要了
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-se",
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"--save_every_epoch",
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type=int,
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required=True,
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help="checkpoint save frequency (epoch)",
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)
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parser.add_argument(
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"-te", "--total_epoch", type=int, required=True, help="total_epoch"
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)
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parser.add_argument(
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"-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
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)
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parser.add_argument(
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"-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
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)
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parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
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parser.add_argument(
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"-bs", "--batch_size", type=int, required=True, help="batch size"
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)
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parser.add_argument(
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"-e", "--experiment_dir", type=str, required=True, help="experiment dir"
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) # -m
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parser.add_argument(
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"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
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)
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parser.add_argument(
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"-sw",
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"--save_every_weights",
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type=str,
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default="0",
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help="save the extracted model in weights directory when saving checkpoints",
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)
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parser.add_argument(
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"-v", "--version", type=str, required=True, help="model version"
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)
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parser.add_argument(
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"-f0",
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"--if_f0",
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type=int,
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required=True,
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help="use f0 as one of the inputs of the model, 1 or 0",
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)
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parser.add_argument(
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"-l",
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"--if_latest",
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type=int,
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required=True,
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help="if only save the latest G/D pth file, 1 or 0",
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)
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parser.add_argument(
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"-c",
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"--if_cache_data_in_gpu",
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type=int,
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required=True,
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help="if caching the dataset in GPU memory, 1 or 0",
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)
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args = parser.parse_args()
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name = args.experiment_dir
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experiment_dir = os.path.join("./logs", args.experiment_dir)
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config_save_path = os.path.join(experiment_dir, "config.json")
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with open(config_save_path, "r") as f:
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config = json.load(f)
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hparams = HParams(**config)
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hparams.model_dir = hparams.experiment_dir = experiment_dir
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hparams.save_every_epoch = args.save_every_epoch
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hparams.name = name
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hparams.total_epoch = args.total_epoch
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hparams.pretrainG = args.pretrainG
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hparams.pretrainD = args.pretrainD
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hparams.version = args.version
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hparams.gpus = args.gpus
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hparams.train.batch_size = args.batch_size
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hparams.sample_rate = args.sample_rate
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hparams.if_f0 = args.if_f0
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hparams.if_latest = args.if_latest
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hparams.save_every_weights = args.save_every_weights
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hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
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hparams.data.training_files = "%s/filelist.txt" % experiment_dir
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return hparams
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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") 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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with open(config_path, "r") 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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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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if type(v) == dict:
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v = HParams(**v)
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self[k] = v
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def keys(self):
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return self.__dict__.keys()
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def items(self):
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return self.__dict__.items()
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def values(self):
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return self.__dict__.values()
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def __len__(self):
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return len(self.__dict__)
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def __getitem__(self, key):
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return getattr(self, key)
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def __setitem__(self, key, value):
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return setattr(self, key, value)
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def __contains__(self, key):
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return key in self.__dict__
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def __repr__(self):
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return self.__dict__.__repr__()
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