ref: b9912f7eb3455926987434db5e3dc97e1a24c1e9
dir: /dnn/torch/osce/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.
*/
"""
seed=1888
import os
import argparse
import sys
import random
random.seed(seed)
import yaml
try:
import git
has_git = True
except:
has_git = False
import torch
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
from torch.optim.lr_scheduler import LambdaLR
import numpy as np
np.random.seed(seed)
from scipy.io import wavfile
from data import SimpleBWESet
from models import model_dict
from engine.bwe_engine import train_one_epoch, evaluate
from utils.bwe_features import load_inference_data
from utils.misc import count_parameters, count_nonzero_parameters
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')
args = parser.parse_args()
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(0)
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")
with open(os.path.join(args.output, 'repo.diff'), "w") as f:
f.write(repo.git.execute(["git", "diff"]))
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']
preemph_gamma = setup['training']['preemph']
# 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=8)
run_validation = True
else:
run_validation = False
# create model
model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs'])
if args.initial_checkpoint is not None:
print(f"loading state dict from {args.initial_checkpoint}...")
chkpt = torch.load(args.initial_checkpoint, map_location='cpu')
model.load_state_dict(chkpt['state_dict'])
# 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)
# push model to device
model.to(device)
# dataloader
dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=8)
# optimizer is introduced to trainable parameters
parameters = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(parameters, lr=lr)
# learning rate scheduler
scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x))
# 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)
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)
tdlp = TDLowpass(15, 4000/24000).to(device)
def criterion(x, y, x_up):
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"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters")
if hasattr(model, 'flop_count'):
print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS")
best_loss = 1e9
for ep in range(1, epochs + 1):
print(f"training epoch {ep}...")
new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, preemph_gamma)
# save checkpoint
checkpoint['state_dict'] = model.state_dict()
checkpoint['loss'] = new_loss
if run_validation:
print("running validation...")
validation_loss = evaluate(model, criterion, validation_dataloader, device, preemph_gamma)
checkpoint['validation_loss'] = validation_loss
if validation_loss < best_loss:
torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth'))
best_loss = validation_loss
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(f"non-zero parameters: {count_nonzero_parameters(model)}\n")
print('Done')