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import os, sys
# sys.path.append('/home/shaunxliu/projects/nnsp')
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import torch
from torch.utils.data import DataLoader
import numpy as np
from .solver import BaseSolver
from utils.data_load import OneshotVcDataset, MultiSpkVcCollate
# from src.rnn_ppg2mel import BiRnnPpg2MelModel
# from src.mel_decoder_mol_encAddlf0 import MelDecoderMOL
from .loss import MaskedMSELoss
from .optim import Optimizer
from utils.util import human_format
from ppg2mel import MelDecoderMOLv2
class Solver(BaseSolver):
"""Customized Solver."""
def __init__(self, config, paras, mode):
super().__init__(config, paras, mode)
self.num_att_plots = 5
self.att_ws_dir = f"{self.logdir}/att_ws"
os.makedirs(self.att_ws_dir, exist_ok=True)
self.best_loss = np.inf
def fetch_data(self, data):
"""Move data to device"""
data = [i.to(self.device) for i in data]
return data
def load_data(self):
""" Load data for training/validation/plotting."""
train_dataset = OneshotVcDataset(
meta_file=self.config.data.train_fid_list,
vctk_ppg_dir=self.config.data.vctk_ppg_dir,
libri_ppg_dir=self.config.data.libri_ppg_dir,
vctk_f0_dir=self.config.data.vctk_f0_dir,
libri_f0_dir=self.config.data.libri_f0_dir,
vctk_wav_dir=self.config.data.vctk_wav_dir,
libri_wav_dir=self.config.data.libri_wav_dir,
vctk_spk_dvec_dir=self.config.data.vctk_spk_dvec_dir,
libri_spk_dvec_dir=self.config.data.libri_spk_dvec_dir,
ppg_file_ext=self.config.data.ppg_file_ext,
min_max_norm_mel=self.config.data.min_max_norm_mel,
mel_min=self.config.data.mel_min,
mel_max=self.config.data.mel_max,
)
dev_dataset = OneshotVcDataset(
meta_file=self.config.data.dev_fid_list,
vctk_ppg_dir=self.config.data.vctk_ppg_dir,
libri_ppg_dir=self.config.data.libri_ppg_dir,
vctk_f0_dir=self.config.data.vctk_f0_dir,
libri_f0_dir=self.config.data.libri_f0_dir,
vctk_wav_dir=self.config.data.vctk_wav_dir,
libri_wav_dir=self.config.data.libri_wav_dir,
vctk_spk_dvec_dir=self.config.data.vctk_spk_dvec_dir,
libri_spk_dvec_dir=self.config.data.libri_spk_dvec_dir,
ppg_file_ext=self.config.data.ppg_file_ext,
min_max_norm_mel=self.config.data.min_max_norm_mel,
mel_min=self.config.data.mel_min,
mel_max=self.config.data.mel_max,
)
self.train_dataloader = DataLoader(
train_dataset,
num_workers=self.paras.njobs,
shuffle=True,
batch_size=self.config.hparas.batch_size,
pin_memory=False,
drop_last=True,
collate_fn=MultiSpkVcCollate(self.config.model.frames_per_step,
use_spk_dvec=True),
)
self.dev_dataloader = DataLoader(
dev_dataset,
num_workers=self.paras.njobs,
shuffle=False,
batch_size=self.config.hparas.batch_size,
pin_memory=False,
drop_last=False,
collate_fn=MultiSpkVcCollate(self.config.model.frames_per_step,
use_spk_dvec=True),
)
self.plot_dataloader = DataLoader(
dev_dataset,
num_workers=self.paras.njobs,
shuffle=False,
batch_size=1,
pin_memory=False,
drop_last=False,
collate_fn=MultiSpkVcCollate(self.config.model.frames_per_step,
use_spk_dvec=True,
give_uttids=True),
)
msg = "Have prepared training set and dev set."
self.verbose(msg)
def load_pretrained_params(self):
print("Load pretrained model from: ", self.config.data.pretrain_model_file)
ignore_layer_prefixes = ["speaker_embedding_table"]
pretrain_model_file = self.config.data.pretrain_model_file
pretrain_ckpt = torch.load(
pretrain_model_file, map_location=self.device
)["model"]
model_dict = self.model.state_dict()
print(self.model)
# 1. filter out unnecessrary keys
for prefix in ignore_layer_prefixes:
pretrain_ckpt = {k : v
for k, v in pretrain_ckpt.items() if not k.startswith(prefix)
}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrain_ckpt)
# 3. load the new state dict
self.model.load_state_dict(model_dict)
def set_model(self):
"""Setup model and optimizer"""
# Model
print("[INFO] Model name: ", self.config["model_name"])
self.model = MelDecoderMOLv2(
**self.config["model"]
).to(self.device)
# self.load_pretrained_params()
# model_params = [{'params': self.model.spk_embedding.weight}]
model_params = [{'params': self.model.parameters()}]
# Loss criterion
self.loss_criterion = MaskedMSELoss(self.config.model.frames_per_step)
# Optimizer
self.optimizer = Optimizer(model_params, **self.config["hparas"])
self.verbose(self.optimizer.create_msg())
# Automatically load pre-trained model if self.paras.load is given
self.load_ckpt()
def exec(self):
self.verbose("Total training steps {}.".format(
human_format(self.max_step)))
mel_loss = None
n_epochs = 0
# Set as current time
self.timer.set()
while self.step < self.max_step:
for data in self.train_dataloader:
# Pre-step: updata lr_rate and do zero_grad
lr_rate = self.optimizer.pre_step(self.step)
total_loss = 0
# data to device
ppgs, lf0_uvs, mels, in_lengths, \
out_lengths, spk_ids, stop_tokens = self.fetch_data(data)
self.timer.cnt("rd")
mel_outputs, mel_outputs_postnet, predicted_stop = self.model(
ppgs,
in_lengths,
mels,
out_lengths,
lf0_uvs,
spk_ids
)
mel_loss, stop_loss = self.loss_criterion(
mel_outputs,
mel_outputs_postnet,
mels,
out_lengths,
stop_tokens,
predicted_stop
)
loss = mel_loss + stop_loss
self.timer.cnt("fw")
# Back-prop
grad_norm = self.backward(loss)
self.step += 1
# Logger
if (self.step == 1) or (self.step % self.PROGRESS_STEP == 0):
self.progress("Tr|loss:{:.4f},mel-loss:{:.4f},stop-loss:{:.4f}|Grad.Norm-{:.2f}|{}"
.format(loss.cpu().item(), mel_loss.cpu().item(),
stop_loss.cpu().item(), grad_norm, self.timer.show()))
self.write_log('loss', {'tr/loss': loss,
'tr/mel-loss': mel_loss,
'tr/stop-loss': stop_loss})
# Validation
if (self.step == 1) or (self.step % self.valid_step == 0):
self.validate()
# End of step
# https://github.com/pytorch/pytorch/issues/13246#issuecomment-529185354
torch.cuda.empty_cache()
self.timer.set()
if self.step > self.max_step:
break
n_epochs += 1
self.log.close()
def validate(self):
self.model.eval()
dev_loss, dev_mel_loss, dev_stop_loss = 0.0, 0.0, 0.0
for i, data in enumerate(self.dev_dataloader):
self.progress('Valid step - {}/{}'.format(i+1, len(self.dev_dataloader)))
# Fetch data
ppgs, lf0_uvs, mels, in_lengths, \
out_lengths, spk_ids, stop_tokens = self.fetch_data(data)
with torch.no_grad():
mel_outputs, mel_outputs_postnet, predicted_stop = self.model(
ppgs,
in_lengths,
mels,
out_lengths,
lf0_uvs,
spk_ids
)
mel_loss, stop_loss = self.loss_criterion(
mel_outputs,
mel_outputs_postnet,
mels,
out_lengths,
stop_tokens,
predicted_stop
)
loss = mel_loss + stop_loss
dev_loss += loss.cpu().item()
dev_mel_loss += mel_loss.cpu().item()
dev_stop_loss += stop_loss.cpu().item()
dev_loss = dev_loss / (i + 1)
dev_mel_loss = dev_mel_loss / (i + 1)
dev_stop_loss = dev_stop_loss / (i + 1)
self.save_checkpoint(f'step_{self.step}.pth', 'loss', dev_loss, show_msg=False)
if dev_loss < self.best_loss:
self.best_loss = dev_loss
self.save_checkpoint(f'best_loss_step_{self.step}.pth', 'loss', dev_loss)
self.write_log('loss', {'dv/loss': dev_loss,
'dv/mel-loss': dev_mel_loss,
'dv/stop-loss': dev_stop_loss})
# plot attention
for i, data in enumerate(self.plot_dataloader):
if i == self.num_att_plots:
break
# Fetch data
ppgs, lf0_uvs, mels, in_lengths, \
out_lengths, spk_ids, stop_tokens = self.fetch_data(data[:-1])
fid = data[-1][0]
with torch.no_grad():
_, _, _, att_ws = self.model(
ppgs,
in_lengths,
mels,
out_lengths,
lf0_uvs,
spk_ids,
output_att_ws=True
)
att_ws = att_ws.squeeze(0).cpu().numpy()
att_ws = att_ws[None]
w, h = plt.figaspect(1.0 / len(att_ws))
fig = plt.Figure(figsize=(w * 1.3, h * 1.3))
axes = fig.subplots(1, len(att_ws))
if len(att_ws) == 1:
axes = [axes]
for ax, aw in zip(axes, att_ws):
ax.imshow(aw.astype(np.float32), aspect="auto")
ax.set_title(f"{fid}")
ax.set_xlabel("Input")
ax.set_ylabel("Output")
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig_name = f"{self.att_ws_dir}/{fid}_step{self.step}.png"
fig.savefig(fig_name)
# Resume training
self.model.train()
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