def train(args):
'''Run training'''
# seed setting
torch.manual_seed(args.seed)
# debug mode setting
# 0 would be fastest, but 1 seems to be reasonable
# by considering reproducability
# revmoe type check
if args.debugmode < 2:
chainer.config.type_check = False
logging.info('torch type check is disabled')
# use determinisitic computation or not
if args.debugmode < 1:
torch.backends.cudnn.deterministic = False
logging.info('torch cudnn deterministic is disabled')
else:
torch.backends.cudnn.deterministic = True
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
idim = int(valid_json[utts[0]]['input'][0]['shape'][1])
odim = int(valid_json[utts[0]]['output'][0]['shape'][1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# specify attention, CTC, hybrid mode
if args.mtlalpha == 1.0:
mtl_mode = 'ctc'
logging.info('Pure CTC mode')
elif args.mtlalpha == 0.0:
mtl_mode = 'att'
logging.info('Pure attention mode')
else:
mtl_mode = 'mtl'
logging.info('Multitask learning mode')
# specify model architecture
e2e = E2E(idim, odim, args)
model = Loss(e2e, args.mtlalpha)
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)), indent=4,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
reporter = model.reporter
# check the use of multi-gpu
if args.ngpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
model = model.to(device)
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = torch.optim.Adadelta(model.parameters(),
rho=0.95,
eps=args.eps)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model.parameters())
# FIXME: TOO DIRTY HACK
setattr(optimizer, "target", reporter)
setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(e2e.subsample[0])
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
# make minibatch list (variable length)
train = make_batchset(train_json, args.batch_size, args.maxlen_in,
args.maxlen_out, args.minibatches)
valid = make_batchset(valid_json, args.batch_size, args.maxlen_in,
args.maxlen_out, args.minibatches)
# hack to make batchsze argument as 1
# actual bathsize is included in a list
train_iter = chainer.iterators.MultiprocessIterator(
TransformDataset(train, converter.transform),
batch_size=1,
n_processes=1,
n_prefetch=8) #, maxtasksperchild=20)
valid_iter = chainer.iterators.SerialIterator(TransformDataset(
valid, converter.transform),
batch_size=1,
repeat=False,
shuffle=False)
# Set up a trainer
updater = CustomUpdater(model, args.grad_clip, train_iter, optimizer,
converter, device, args.ngpu)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device))
# Save attention weight each epoch
if args.num_save_attention > 0 and args.mtlalpha != 1.0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.predictor.calculate_all_attentions
else:
att_vis_fn = model.predictor.calculate_all_attentions
trainer.extend(PlotAttentionReport(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
device=device),
trigger=(1, 'epoch'))
# Make a plot for training and validation values
trainer.extend(
extensions.PlotReport([
'main/loss', 'validation/main/loss', 'main/loss_ctc',
'validation/main/loss_ctc', 'main/loss_att',
'validation/main/loss_att'
],
'epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/acc', 'validation/main/acc'],
'epoch',
file_name='acc.png'))
# Save best models
trainer.extend(
extensions.snapshot_object(model,
'model.loss.best',
savefun=torch_save),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode is not 'ctc':
trainer.extend(
extensions.snapshot_object(model,
'model.acc.best',
savefun=torch_save),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# save snapshot which contains model and optimizer states
trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode is not 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
elif args.criterion == 'loss':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.loss.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(trigger=(REPORT_INTERVAL,
'iteration')))
report_keys = [
'epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
'validation/main/loss', 'validation/main/loss_ctc',
'validation/main/loss_att', 'main/acc', 'validation/main/acc',
'elapsed_time'
]
if args.opt == 'adadelta':
trainer.extend(extensions.observe_value(
'eps', lambda trainer: trainer.updater.get_optimizer('main').
param_groups[0]["eps"]),
trigger=(REPORT_INTERVAL, 'iteration'))
report_keys.append('eps')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(REPORT_INTERVAL, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=REPORT_INTERVAL))
# Run the training
trainer.run()
def train(args):
'''RUN TRAINING'''
# seed setting
torch.manual_seed(args.seed)
# use determinisitic computation or not
if args.debugmode < 1:
torch.backends.cudnn.deterministic = False
logging.info('torch cudnn deterministic is disabled')
else:
torch.backends.cudnn.deterministic = True
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
# reverse input and output dimension
idim = int(valid_json[utts[0]]['output'][0]['shape'][1])
odim = int(valid_json[utts[0]]['input'][0]['shape'][1])
if args.use_cbhg:
args.spc_dim = int(valid_json[utts[0]]['input'][1]['shape'][1])
if args.use_speaker_embedding:
args.spk_embed_dim = int(valid_json[utts[0]]['input'][1]['shape'][0])
else:
args.spk_embed_dim = None
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)), indent=4,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# specify model architecture
tacotron2 = Tacotron2(idim, odim, args)
logging.info(tacotron2)
# check the use of multi-gpu
if args.ngpu > 1:
tacotron2 = torch.nn.DataParallel(tacotron2,
device_ids=list(range(args.ngpu)))
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
tacotron2 = tacotron2.to(device)
# define loss
model = Tacotron2Loss(tacotron2, args.use_masking, args.bce_pos_weight)
reporter = model.reporter
# Setup an optimizer
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
eps=args.eps,
weight_decay=args.weight_decay)
# FIXME: TOO DIRTY HACK
setattr(optimizer, 'target', reporter)
setattr(optimizer, 'serialize', lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(True, args.use_speaker_embedding,
args.use_cbhg)
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
# make minibatch list (variable length)
train_batchset = make_batchset(train_json, args.batch_size, args.maxlen_in,
args.maxlen_out, args.minibatches,
args.batch_sort_key)
valid_batchset = make_batchset(valid_json, args.batch_size, args.maxlen_in,
args.maxlen_out, args.minibatches,
args.batch_sort_key)
# hack to make batchsze argument as 1
# actual bathsize is included in a list
train_iter = chainer.iterators.MultiprocessIterator(TransformDataset(
train_batchset, converter.transform),
batch_size=1,
n_processes=2,
n_prefetch=8,
maxtasksperchild=20)
valid_iter = chainer.iterators.MultiprocessIterator(TransformDataset(
valid_batchset, converter.transform),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=2,
n_prefetch=8,
maxtasksperchild=20)
# Set up a trainer
updater = CustomUpdater(model, args.grad_clip, train_iter, optimizer,
converter, device)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device))
# Save snapshot for each epoch
trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
# Save best models
trainer.extend(
extensions.snapshot_object(tacotron2,
'model.loss.best',
savefun=torch_save),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
# Save attention figure for each epoch
if args.num_save_attention > 0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(tacotron2, "module"):
att_vis_fn = tacotron2.module.calculate_all_attentions
else:
att_vis_fn = tacotron2.calculate_all_attentions
trainer.extend(PlotAttentionReport(att_vis_fn,
data,
args.outdir + '/att_ws',
converter=CustomConverter(
False,
args.use_speaker_embedding),
device=device,
reverse=True),
trigger=(1, 'epoch'))
# Make a plot for training and validation values
plot_keys = [
'main/loss', 'validation/main/loss', 'main/l1_loss',
'validation/main/l1_loss', 'main/mse_loss', 'validation/main/mse_loss',
'main/bce_loss', 'validation/main/bce_loss'
]
trainer.extend(
extensions.PlotReport(['main/l1_loss', 'validation/main/l1_loss'],
'epoch',
file_name='l1_loss.png'))
trainer.extend(
extensions.PlotReport(['main/mse_loss', 'validation/main/mse_loss'],
'epoch',
file_name='mse_loss.png'))
trainer.extend(
extensions.PlotReport(['main/bce_loss', 'validation/main/bce_loss'],
'epoch',
file_name='bce_loss.png'))
if args.use_cbhg:
plot_keys += [
'main/cbhg_l1_loss', 'validation/main/cbhg_l1_loss',
'main/cbhg_mse_loss', 'validation/main/cbhg_mse_loss'
]
trainer.extend(
extensions.PlotReport(
['main/cbhg_l1_loss', 'validation/main/cbhg_l1_loss'],
'epoch',
file_name='cbhg_l1_loss.png'))
trainer.extend(
extensions.PlotReport(
['main/cbhg_mse_loss', 'validation/main/cbhg_mse_loss'],
'epoch',
file_name='cbhg_mse_loss.png'))
trainer.extend(
extensions.PlotReport(plot_keys, 'epoch', file_name='loss.png'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(trigger=(REPORT_INTERVAL,
'iteration')))
report_keys = plot_keys[:]
report_keys[0:0] = ['epoch', 'iteration', 'elapsed_time']
trainer.extend(extensions.PrintReport(report_keys),
trigger=(REPORT_INTERVAL, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=REPORT_INTERVAL))
# Run the training
trainer.run()
def train(args):
# display torch version
logging.info('torch version = ' + torch.__version__)
# seed setting
nseed = args.seed
torch.manual_seed(nseed)
logging.info('torch seed = ' + str(nseed))
# debug mode setting
# 0 would be fastest, but 1 seems to be reasonable
# by considering reproducability
# use determinisitic computation or not
if args.debugmode < 1:
torch.backends.cudnn.deterministic = False
logging.info('torch cudnn deterministic is disabled')
else:
torch.backends.cudnn.deterministic = True
# check cuda and cudnn availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get special label ids
unk = args.char_list_dict['<unk>']
eos = args.char_list_dict['<eos>']
# read tokens as a sequence of sentences
train = read_tokens(args.train_label, args.char_list_dict)
val = read_tokens(args.valid_label, args.char_list_dict)
# count tokens
n_train_tokens, n_train_oovs = count_tokens(train, unk)
n_val_tokens, n_val_oovs = count_tokens(val, unk)
logging.info('#vocab = ' + str(args.n_vocab))
logging.info('#sentences in the training data = ' + str(len(train)))
logging.info('#tokens in the training data = ' + str(n_train_tokens))
logging.info('oov rate in the training data = %.2f %%' %
(n_train_oovs / n_train_tokens * 100))
logging.info('#sentences in the validation data = ' + str(len(val)))
logging.info('#tokens in the validation data = ' + str(n_val_tokens))
logging.info('oov rate in the validation data = %.2f %%' %
(n_val_oovs / n_val_tokens * 100))
# Create the dataset iterators
train_iter = ParallelSentenceIterator(train,
args.batchsize,
max_length=args.maxlen,
sos=eos,
eos=eos)
val_iter = ParallelSentenceIterator(val,
args.batchsize,
max_length=args.maxlen,
sos=eos,
eos=eos,
repeat=False)
logging.info('#iterations per epoch = ' +
str(len(train_iter.batch_indices)))
logging.info('#total iterations = ' +
str(args.epoch * len(train_iter.batch_indices)))
# Prepare an RNNLM model
rnn = RNNLM(args.n_vocab, args.layer, args.unit)
model = ClassifierWithState(rnn)
if args.ngpu > 1:
logging.warn("currently, multi-gpu is not supported. use single gpu.")
if args.ngpu > 0:
# Make the specified GPU current
gpu_id = 0
model.cuda(gpu_id)
else:
gpu_id = -1
# Save model conf to json
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps(vars(args), indent=4, sort_keys=True).encode('utf_8'))
# Set up an optimizer
if args.opt == 'sgd':
optimizer = torch.optim.SGD(model.parameters(), lr=1.0)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model.parameters())
# FIXME: TOO DIRTY HACK
reporter = model.reporter
setattr(optimizer, "target", reporter)
setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
updater = BPTTUpdater(train_iter,
model,
optimizer,
gpu_id,
gradclip=args.gradclip)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.outdir)
trainer.extend(LMEvaluator(val_iter, model, reporter, device=gpu_id))
trainer.extend(
extensions.LogReport(postprocess=compute_perplexity,
trigger=(REPORT_INTERVAL, 'iteration')))
trainer.extend(extensions.PrintReport(
['epoch', 'iteration', 'perplexity', 'val_perplexity',
'elapsed_time']),
trigger=(REPORT_INTERVAL, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=REPORT_INTERVAL))
# Save best models
trainer.extend(torch_snapshot(filename='snapshot.ep.{.updater.epoch}'))
trainer.extend(
extensions.snapshot_object(model,
'rnnlm.model.{.updater.epoch}',
savefun=torch_save))
# T.Hori: MinValueTrigger should be used, but it fails when resuming
trainer.extend(
MakeSymlinkToBestModel('validation/main/loss', 'rnnlm.model'))
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
trainer.run()
# compute perplexity for test set
if args.test_label:
logging.info('test the best model')
torch_load(args.outdir + '/rnnlm.model.best', model)
test = read_tokens(args.test_label, args.char_list_dict)
n_test_tokens, n_test_oovs = count_tokens(test, unk)
logging.info('#sentences in the test data = ' + str(len(test)))
logging.info('#tokens in the test data = ' + str(n_test_tokens))
logging.info('oov rate in the test data = %.2f %%' %
(n_test_oovs / n_test_tokens * 100))
test_iter = ParallelSentenceIterator(test,
args.batchsize,
max_length=args.maxlen,
sos=eos,
eos=eos,
repeat=False)
evaluator = LMEvaluator(test_iter, model, reporter, device=gpu_id)
result = evaluator()
logging.info('test perplexity: ' +
str(np.exp(float(result['main/loss']))))
