ref: b9912f7eb3455926987434db5e3dc97e1a24c1e9
dir: /dnn/torch/osce/adv_train_bwe_model.py/
"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import os
import argparse
import sys
import math as m
import random
import yaml
from tqdm import tqdm
try:
import git
has_git = True
except:
has_git = False
import torch
from torch.optim.lr_scheduler import LambdaLR
import torch.nn.functional as F
from scipy.io import wavfile
import numpy as np
import pesq
from data import SimpleBWESet
from models import model_dict
from utils.bwe_features import load_inference_data
from utils.misc import count_parameters, retain_grads, get_grad_norm, create_weights
from losses.stft_loss import MRSTFTLoss, MRLogMelLoss
from losses.td_lowpass import TDLowpass
parser = argparse.ArgumentParser()
parser.add_argument('setup', type=str, help='setup yaml file')
parser.add_argument('output', type=str, help='output path')
parser.add_argument('--device', type=str, help='compute device', default=None)
parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None)
parser.add_argument('--testdata', type=str, help='path to features and signal for testing', default=None)
parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout')
parser.add_argument('--initial-epoch', type=int, help='initial epoch number for training', default=1)
args = parser.parse_args()
def preemph(x, gamma):
y = torch.cat((x[..., 0:1], x[..., 1:] - gamma * x[...,:-1]), dim=-1)
return y
torch.set_num_threads(4)
with open(args.setup, 'r') as f:
setup = yaml.load(f.read(), yaml.FullLoader)
checkpoint_prefix = 'checkpoint'
output_prefix = 'output'
setup_name = 'setup.yml'
output_file='out.txt'
# check model
if not 'name' in setup['model']:
print(f'warning: did not find model entry in setup, using default PitchPostFilter')
model_name = 'pitchpostfilter'
else:
model_name = setup['model']['name']
# prepare output folder
if os.path.exists(args.output):
print("warning: output folder exists")
reply = input('continue? (y/n): ')
while reply not in {'y', 'n'}:
reply = input('continue? (y/n): ')
if reply == 'n':
os._exit()
else:
os.makedirs(args.output, exist_ok=True)
checkpoint_dir = os.path.join(args.output, 'checkpoints')
os.makedirs(checkpoint_dir, exist_ok=True)
# add repo info to setup
if has_git:
working_dir = os.path.split(__file__)[0]
try:
repo = git.Repo(working_dir, search_parent_directories=True)
setup['repo'] = dict()
hash = repo.head.object.hexsha
urls = list(repo.remote().urls)
is_dirty = repo.is_dirty()
if is_dirty:
print("warning: repo is dirty")
setup['repo']['hash'] = hash
setup['repo']['urls'] = urls
setup['repo']['dirty'] = is_dirty
except:
has_git = False
# dump setup
with open(os.path.join(args.output, setup_name), 'w') as f:
yaml.dump(setup, f)
if args.testdata is not None:
testsignal, features = load_inference_data(args.testdata, **setup['data'])
inference_test = True
inference_folder = os.path.join(args.output, 'inference_test')
os.makedirs(os.path.join(args.output, 'inference_test'), exist_ok=True)
else:
inference_test = False
# training parameters
batch_size = setup['training']['batch_size']
epochs = setup['training']['epochs']
lr = setup['training']['lr']
lr_decay_factor = setup['training']['lr_decay_factor']
lr_gen = lr * setup['training']['gen_lr_reduction']
lambda_feat = setup['training']['lambda_feat']
lambda_reg = setup['training']['lambda_reg']
adv_target = setup['training'].get('adv_target', 'x_48')
newloss = setup['training'].get('newloss', False)
# load training dataset
data_config = setup['data']
data = SimpleBWESet(setup['dataset'], **data_config)
# load validation dataset if given
if 'validation_dataset' in setup:
validation_data = SimpleBWESet(setup['validation_dataset'], **data_config)
validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4)
run_validation = True
else:
run_validation = False
# create model
model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs'])
# create discriminator
print(setup['discriminator']['name'],setup['discriminator']['kwargs'])
disc_name = setup['discriminator']['name']
disc = model_dict[disc_name](
*setup['discriminator']['args'], **setup['discriminator']['kwargs']
)
# set compute device
if type(args.device) == type(None):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
device = torch.device(args.device)
# dataloader
dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4)
# optimizer is introduced to trainable parameters
parameters = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(parameters, lr=lr_gen)
# disc optimizer
parameters = [p for p in disc.parameters() if p.requires_grad]
optimizer_disc = torch.optim.Adam(parameters, lr=lr, betas=[0.5, 0.9])
# learning rate scheduler
scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x))
if args.initial_checkpoint is not None:
print(f"loading state dict from {args.initial_checkpoint}...")
chkpt = torch.load(args.initial_checkpoint, map_location=device)
model.load_state_dict(chkpt['state_dict'])
if 'disc_state_dict' in chkpt:
print(f"loading discriminator state dict from {args.initial_checkpoint}...")
disc.load_state_dict(chkpt['disc_state_dict'])
if 'optimizer_state_dict' in chkpt:
print(f"loading optimizer state dict from {args.initial_checkpoint}...")
optimizer.load_state_dict(chkpt['optimizer_state_dict'])
if 'disc_optimizer_state_dict' in chkpt:
print(f"loading discriminator optimizer state dict from {args.initial_checkpoint}...")
optimizer_disc.load_state_dict(chkpt['disc_optimizer_state_dict'])
if 'scheduler_state_disc' in chkpt:
print(f"loading scheduler state dict from {args.initial_checkpoint}...")
scheduler.load_state_dict(chkpt['scheduler_state_dict'])
# if 'torch_rng_state' in chkpt:
# print(f"setting torch RNG state from {args.initial_checkpoint}...")
# torch.set_rng_state(chkpt['torch_rng_state'])
if 'numpy_rng_state' in chkpt:
print(f"setting numpy RNG state from {args.initial_checkpoint}...")
np.random.set_state(chkpt['numpy_rng_state'])
if 'python_rng_state' in chkpt:
print(f"setting Python RNG state from {args.initial_checkpoint}...")
random.setstate(chkpt['python_rng_state'])
# loss
w_l1 = setup['training']['loss']['w_l1']
w_lm = setup['training']['loss']['w_lm']
w_slm = setup['training']['loss']['w_slm']
w_sc = setup['training']['loss']['w_sc']
w_logmel = setup['training']['loss']['w_logmel']
w_wsc = setup['training']['loss']['w_wsc']
w_xcorr = setup['training']['loss']['w_xcorr']
w_sxcorr = setup['training']['loss']['w_sxcorr']
w_l2 = setup['training']['loss']['w_l2']
w_tdlp = setup['training']['loss'].get('w_tdlp', 0)
preemph_gamma = setup['training']['preemph']
w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 + w_tdlp
fft_sizes_16k = [2048, 1024, 512, 256, 128, 64]
fft_sizes_48k = [3 * n for n in fft_sizes_16k]
stftloss = MRSTFTLoss(fft_sizes=fft_sizes_48k, fs=48000, sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device)
logmelloss = MRLogMelLoss(fft_sizes=fft_sizes_48k).to(device)
def xcorr_loss(y_true, y_pred):
dims = list(range(1, len(y_true.shape)))
loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9)
return torch.mean(loss)
def td_l2_norm(y_true, y_pred):
dims = list(range(1, len(y_true.shape)))
loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6)
return loss.mean()
def td_l1(y_true, y_pred, pow=0):
dims = list(range(1, len(y_true.shape)))
tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow)
return torch.mean(tmp)
if newloss:
tdlp = TDLowpass(31, 4000/24000).to(device)
else:
tdlp = TDLowpass(15, 4000/24000).to(device)
if newloss:
def criterion(x, y, x_up):
# FD-losses are calculated on preemphasized signals
xp = preemph(x, preemph_gamma)
yp = preemph(y, preemph_gamma)
return (w_l1 * td_l1(x, y, pow=1) + stftloss(xp, yp) + w_logmel * logmelloss(xp, yp)
+ w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y) + w_tdlp * tdlp(x_up, y)) / w_sum
else:
def criterion(x, y, x_up):
# all losses are calculated on preemphasized signals
x = preemph(x, preemph_gamma)
y = preemph(y, preemph_gamma)
x_up = preemph(x_up, preemph_gamma)
return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y)
+ w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y) + w_tdlp * tdlp(x_up, y)) / w_sum
# model checkpoint
checkpoint = {
'setup' : setup,
'state_dict' : model.state_dict(),
'loss' : -1
}
if not args.no_redirect:
print(f"re-directing output to {os.path.join(args.output, output_file)}")
sys.stdout = open(os.path.join(args.output, output_file), "w")
print("summary:")
print(f"generator: {count_parameters(model.cpu()) / 1e6:5.3f} M parameters")
if hasattr(model, 'flop_count'):
print(f"generator: {model.flop_count(16000) / 1e6:5.3f} MFLOPS")
print(f"discriminator: {count_parameters(disc.cpu()) / 1e6:5.3f} M parameters")
best_loss = 1e9
log_interval = 10
m_r = 0
m_f = 0
s_r = 1
s_f = 1
def optimizer_to(optim, device):
for param in optim.state.values():
if isinstance(param, torch.Tensor):
param.data = param.data.to(device)
if param._grad is not None:
param._grad.data = param._grad.data.to(device)
elif isinstance(param, dict):
for subparam in param.values():
if isinstance(subparam, torch.Tensor):
subparam.data = subparam.data.to(device)
if subparam._grad is not None:
subparam._grad.data = subparam._grad.data.to(device)
optimizer_to(optimizer, device)
optimizer_to(optimizer_disc, device)
retain_grads(model)
retain_grads(disc)
for ep in range(args.initial_epoch, epochs + 1):
print(f"training epoch {ep}...")
model.to(device)
disc.to(device)
model.train()
disc.train()
running_disc_loss = 0
running_adv_loss = 0
running_feature_loss = 0
running_reg_loss = 0
running_disc_grad_norm = 0
running_model_grad_norm = 0
with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
for i, batch in enumerate(tepoch):
# set gradients to zero
optimizer.zero_grad()
# push batch to device
for key in batch:
batch[key] = batch[key].to(device)
target = batch['x_48']
x16 = batch['x_16'].unsqueeze(1)
x_up = model.upsampler(x16)
disc_target = batch['x_48']
# calculate model output
output = model(x16, batch['features'])
# pre-emphasize
disc_target = preemph(target, preemph_gamma)
output_preemph = preemph(output, preemph_gamma)
# discriminator update
scores_gen = disc(output_preemph.detach())
scores_real = disc(disc_target.unsqueeze(1))
disc_loss = 0
for score in scores_gen:
disc_loss += (((score[-1]) ** 2)).mean()
m_f = 0.9 * m_f + 0.1 * score[-1].detach().mean().cpu().item()
s_f = 0.9 * s_f + 0.1 * score[-1].detach().std().cpu().item()
for score in scores_real:
disc_loss += (((1 - score[-1]) ** 2)).mean()
m_r = 0.9 * m_r + 0.1 * score[-1].detach().mean().cpu().item()
s_r = 0.9 * s_r + 0.1 * score[-1].detach().std().cpu().item()
disc_loss = 0.5 * disc_loss / len(scores_gen)
winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) )
disc.zero_grad()
disc_loss.backward()
running_disc_grad_norm += get_grad_norm(disc).detach().cpu().item()
optimizer_disc.step()
# generator update
scores_gen = disc(output_preemph)
# calculate loss
loss_reg = criterion(target, output.squeeze(1), x_up)
num_discs = len(scores_gen)
gen_loss = 0
for score in scores_gen:
gen_loss += (((1 - score[-1]) ** 2)).mean() / num_discs
loss_feat = 0
for k in range(num_discs):
num_layers = len(scores_gen[k]) - 1
f = 4 / num_discs / num_layers
for l in range(num_layers):
loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach())
model.zero_grad()
(gen_loss + lambda_feat * loss_feat + lambda_reg * loss_reg).backward()
optimizer.step()
# sparsification
if hasattr(model, 'sparsifier'):
model.sparsifier()
running_model_grad_norm += get_grad_norm(model).detach().cpu().item()
running_adv_loss += gen_loss.detach().cpu().item()
running_disc_loss += disc_loss.detach().cpu().item()
running_feature_loss += lambda_feat * loss_feat.detach().cpu().item()
running_reg_loss += lambda_reg * loss_reg.detach().cpu().item()
# update status bar
if i % log_interval == 0:
tepoch.set_postfix(adv_loss=f"{running_adv_loss/(i + 1):8.7f}",
disc_loss=f"{running_disc_loss/(i + 1):8.7f}",
feat_loss=f"{running_feature_loss/(i + 1):8.7f}",
reg_loss=f"{running_reg_loss/(i + 1):8.7f}",
model_gradnorm=f"{running_model_grad_norm/(i+1):8.7f}",
disc_gradnorm=f"{running_disc_grad_norm/(i+1):8.7f}",
wc=f"{100*winning_chance:5.2f}%")
# save checkpoint
checkpoint['state_dict'] = model.state_dict()
checkpoint['disc_state_dict'] = disc.state_dict()
checkpoint['optimizer_state_dict'] = optimizer.state_dict()
checkpoint['disc_optimizer_state_dict'] = optimizer_disc.state_dict()
checkpoint['scheduler_state_dict'] = scheduler.state_dict()
checkpoint['torch_rng_state'] = torch.get_rng_state()
checkpoint['numpy_rng_state'] = np.random.get_state()
checkpoint['python_rng_state'] = random.getstate()
checkpoint['adv_loss'] = running_adv_loss/(i + 1)
checkpoint['disc_loss'] = running_disc_loss/(i + 1)
checkpoint['feature_loss'] = running_feature_loss/(i + 1)
checkpoint['reg_loss'] = running_reg_loss/(i + 1)
if inference_test:
print("running inference test...")
with torch.no_grad():
out = model(testsignal.to(device).view(1, 1, -1), features.to(device).unsqueeze(0)).cpu().squeeze().numpy()
wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 48000, (2**15 * out).astype(np.int16))
torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth'))
torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth'))
print()
print('Done')