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import argparse
from datetime import datetime
import json
import logging
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.utils.data
from lib import dataset
from lib import nets
from lib import spec_utils
def setup_logger(name, logfile='LOGFILENAME.log'):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
logger.propagate = False
fh = logging.FileHandler(logfile, encoding='utf8')
fh.setLevel(logging.DEBUG)
fh_formatter = logging.Formatter(
'%(asctime)s - %(levelname)s - %(message)s')
fh.setFormatter(fh_formatter)
sh = logging.StreamHandler()
sh.setLevel(logging.INFO)
logger.addHandler(fh)
logger.addHandler(sh)
return logger
def train_epoch(dataloader, model, device, optimizer, accumulation_steps):
model.train()
sum_loss = 0
crit = nn.L1Loss()
for itr, (X_batch, y_batch) in enumerate(dataloader):
X_batch = X_batch.to(device)
y_batch = y_batch.to(device)
pred, aux = model(X_batch)
loss_main = crit(pred * X_batch, y_batch)
loss_aux = crit(aux * X_batch, y_batch)
loss = loss_main * 0.8 + loss_aux * 0.2
accum_loss = loss / accumulation_steps
accum_loss.backward()
if (itr + 1) % accumulation_steps == 0:
optimizer.step()
model.zero_grad()
sum_loss += loss.item() * len(X_batch)
# the rest batch
if (itr + 1) % accumulation_steps != 0:
optimizer.step()
model.zero_grad()
return sum_loss / len(dataloader.dataset)
def validate_epoch(dataloader, model, device):
model.eval()
sum_loss = 0
crit = nn.L1Loss()
with torch.no_grad():
for X_batch, y_batch in dataloader:
X_batch = X_batch.to(device)
y_batch = y_batch.to(device)
pred = model.predict(X_batch)
y_batch = spec_utils.crop_center(y_batch, pred)
loss = crit(pred, y_batch)
sum_loss += loss.item() * len(X_batch)
return sum_loss / len(dataloader.dataset)
def main():
p = argparse.ArgumentParser()
p.add_argument('--gpu', '-g', type=int, default=-1)
p.add_argument('--seed', '-s', type=int, default=2019)
p.add_argument('--sr', '-r', type=int, default=44100)
p.add_argument('--hop_length', '-H', type=int, default=1024)
p.add_argument('--n_fft', '-f', type=int, default=2048)
p.add_argument('--dataset', '-d', required=True)
p.add_argument('--split_mode', '-S', type=str, choices=['random', 'subdirs'], default='random')
p.add_argument('--learning_rate', '-l', type=float, default=0.001)
p.add_argument('--lr_min', type=float, default=0.0001)
p.add_argument('--lr_decay_factor', type=float, default=0.9)
p.add_argument('--lr_decay_patience', type=int, default=6)
p.add_argument('--batchsize', '-B', type=int, default=4)
p.add_argument('--accumulation_steps', '-A', type=int, default=1)
p.add_argument('--cropsize', '-C', type=int, default=256)
p.add_argument('--patches', '-p', type=int, default=16)
p.add_argument('--val_rate', '-v', type=float, default=0.2)
p.add_argument('--val_filelist', '-V', type=str, default=None)
p.add_argument('--val_batchsize', '-b', type=int, default=6)
p.add_argument('--val_cropsize', '-c', type=int, default=256)
p.add_argument('--num_workers', '-w', type=int, default=6)
p.add_argument('--epoch', '-E', type=int, default=200)
p.add_argument('--reduction_rate', '-R', type=float, default=0.0)
p.add_argument('--reduction_level', '-L', type=float, default=0.2)
p.add_argument('--mixup_rate', '-M', type=float, default=0.0)
p.add_argument('--mixup_alpha', '-a', type=float, default=1.0)
p.add_argument('--pretrained_model', '-P', type=str, default=None)
p.add_argument('--debug', action='store_true')
args = p.parse_args()
logger.debug(vars(args))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
val_filelist = []
if args.val_filelist is not None:
with open(args.val_filelist, 'r', encoding='utf8') as f:
val_filelist = json.load(f)
train_filelist, val_filelist = dataset.train_val_split(
dataset_dir=args.dataset,
split_mode=args.split_mode,
val_rate=args.val_rate,
val_filelist=val_filelist
)
if args.debug:
logger.info('### DEBUG MODE')
train_filelist = train_filelist[:1]
val_filelist = val_filelist[:1]
elif args.val_filelist is None and args.split_mode == 'random':
with open('val_{}.json'.format(timestamp), 'w', encoding='utf8') as f:
json.dump(val_filelist, f, ensure_ascii=False)
for i, (X_fname, y_fname) in enumerate(val_filelist):
logger.info('{} {} {}'.format(i + 1, os.path.basename(X_fname), os.path.basename(y_fname)))
device = torch.device('cpu')
model = nets.CascadedNet(args.n_fft, 32, 128)
if args.pretrained_model is not None:
model.load_state_dict(torch.load(args.pretrained_model, map_location=device))
if torch.cuda.is_available() and args.gpu >= 0:
device = torch.device('cuda:{}'.format(args.gpu))
model.to(device)
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=args.learning_rate
)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=args.lr_decay_factor,
patience=args.lr_decay_patience,
threshold=1e-6,
min_lr=args.lr_min,
verbose=True
)
bins = args.n_fft // 2 + 1
freq_to_bin = 2 * bins / args.sr
unstable_bins = int(200 * freq_to_bin)
stable_bins = int(22050 * freq_to_bin)
reduction_weight = np.concatenate([
np.linspace(0, 1, unstable_bins, dtype=np.float32)[:, None],
np.linspace(1, 0, stable_bins - unstable_bins, dtype=np.float32)[:, None],
np.zeros((bins - stable_bins, 1), dtype=np.float32),
], axis=0) * args.reduction_level
training_set = dataset.make_training_set(
filelist=train_filelist,
sr=args.sr,
hop_length=args.hop_length,
n_fft=args.n_fft
)
train_dataset = dataset.VocalRemoverTrainingSet(
training_set * args.patches,
cropsize=args.cropsize,
reduction_rate=args.reduction_rate,
reduction_weight=reduction_weight,
mixup_rate=args.mixup_rate,
mixup_alpha=args.mixup_alpha
)
train_dataloader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=args.batchsize,
shuffle=True,
num_workers=args.num_workers
)
patch_list = dataset.make_validation_set(
filelist=val_filelist,
cropsize=args.val_cropsize,
sr=args.sr,
hop_length=args.hop_length,
n_fft=args.n_fft,
offset=model.offset
)
val_dataset = dataset.VocalRemoverValidationSet(
patch_list=patch_list
)
val_dataloader = torch.utils.data.DataLoader(
dataset=val_dataset,
batch_size=args.val_batchsize,
shuffle=False,
num_workers=args.num_workers
)
log = []
best_loss = np.inf
for epoch in range(args.epoch):
logger.info('# epoch {}'.format(epoch))
train_loss = train_epoch(train_dataloader, model, device, optimizer, args.accumulation_steps)
val_loss = validate_epoch(val_dataloader, model, device)
logger.info(
' * training loss = {:.6f}, validation loss = {:.6f}'
.format(train_loss, val_loss)
)
scheduler.step(val_loss)
if val_loss < best_loss:
best_loss = val_loss
logger.info(' * best validation loss')
model_path = 'models/model_iter{}.pth'.format(epoch)
torch.save(model.state_dict(), model_path)
log.append([train_loss, val_loss])
with open('loss_{}.json'.format(timestamp), 'w', encoding='utf8') as f:
json.dump(log, f, ensure_ascii=False)
if __name__ == '__main__':
timestamp = datetime.now().strftime('%Y%m%d%H%M%S')
logger = setup_logger(__name__, 'train_{}.log'.format(timestamp))
try:
main()
except Exception as e:
logger.exception(e)
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