def custom_updater(iters, optimizer, converter, device=-1, accum_grad=1):
"""Get custom_updater of the model."""
return CustomUpdater(iters,
optimizer,
converter=converter,
device=device,
accum_grad=accum_grad)
def custom_updater(iters, optimizer, converter, device=-1, accum_grad=1):
"""Get custom_updater of the model."""
from espnet.nets.chainer_backend.transformer.training import CustomUpdater
return CustomUpdater(iters,
optimizer,
converter=converter,
device=device,
accum_grad=accum_grad)
def train(args):
"""Train with the given args.
Args:
args (namespace): The program arguments.
"""
# display chainer version
logging.info('chainer version = ' + chainer.__version__)
set_deterministic_chainer(args)
# check cuda and cudnn availability
if not chainer.cuda.available:
logging.warning('cuda is not available')
if not chainer.cuda.cudnn_enabled:
logging.warning('cudnn 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
logging.info('import model module: ' + args.model_module)
model_class = dynamic_import(args.model_module)
model = model_class(idim, odim, args, flag_return=False)
assert isinstance(model, ASRInterface)
# 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,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# Set gpu
ngpu = args.ngpu
if ngpu == 1:
gpu_id = 0
# Make a specified GPU current
chainer.cuda.get_device_from_id(gpu_id).use()
model.to_gpu() # Copy the model to the GPU
logging.info('single gpu calculation.')
elif ngpu > 1:
gpu_id = 0
devices = {'main': gpu_id}
for gid in six.moves.xrange(1, ngpu):
devices['sub_%d' % gid] = gid
logging.info('multi gpu calculation (#gpus = %d).' % ngpu)
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
else:
gpu_id = -1
logging.info('cpu calculation')
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = chainer.optimizers.AdaDelta(eps=args.eps)
elif args.opt == 'adam':
optimizer = chainer.optimizers.Adam()
elif args.opt == 'noam':
optimizer = chainer.optimizers.Adam(alpha=0,
beta1=0.9,
beta2=0.98,
eps=1e-9)
else:
raise NotImplementedError('args.opt={}'.format(args.opt))
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.grad_clip))
# Setup Training Extensions
if 'transformer' in args.model_module:
from espnet.nets.chainer_backend.transformer.training import CustomConverter
from espnet.nets.chainer_backend.transformer.training import CustomParallelUpdater
from espnet.nets.chainer_backend.transformer.training import CustomUpdater
else:
from espnet.nets.chainer_backend.rnn.training import CustomConverter
from espnet.nets.chainer_backend.rnn.training import CustomParallelUpdater
from espnet.nets.chainer_backend.rnn.training import CustomUpdater
# Setup a converter
converter = CustomConverter(subsampling_factor=model.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']
# set up training iterator and updater
load_tr = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': True} # Switch the mode of preprocessing
)
load_cv = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': False} # Switch the mode of preprocessing
)
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
accum_grad = args.accum_grad
if ngpu <= 1:
# make minibatch list (variable length)
train = make_batchset(train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
# hack to make batchsize argument as 1
# actual batchsize is included in a list
if args.n_iter_processes > 0:
train_iters = [
ToggleableShufflingMultiprocessIterator(
TransformDataset(train, load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
]
else:
train_iters = [
ToggleableShufflingSerialIterator(TransformDataset(
train, load_tr),
batch_size=1,
shuffle=not use_sortagrad)
]
# set up updater
updater = CustomUpdater(train_iters[0],
optimizer,
converter=converter,
device=gpu_id,
accum_grad=accum_grad)
else:
if args.batch_count not in ("auto", "seq") and args.batch_size == 0:
raise NotImplementedError(
"--batch-count 'bin' and 'frame' are not implemented in chainer multi gpu"
)
# set up minibatches
train_subsets = []
for gid in six.moves.xrange(ngpu):
# make subset
train_json_subset = {
k: v
for i, (k, v) in enumerate(train_json.items())
if i % ngpu == gid
}
# make minibatch list (variable length)
train_subsets += [
make_batchset(train_json_subset, args.batch_size,
args.maxlen_in, args.maxlen_out,
args.minibatches)
]
# each subset must have same length for MultiprocessParallelUpdater
maxlen = max([len(train_subset) for train_subset in train_subsets])
for train_subset in train_subsets:
if maxlen != len(train_subset):
for i in six.moves.xrange(maxlen - len(train_subset)):
train_subset += [train_subset[i]]
# hack to make batchsize argument as 1
# actual batchsize is included in a list
if args.n_iter_processes > 0:
train_iters = [
ToggleableShufflingMultiprocessIterator(
TransformDataset(train_subsets[gid], load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
for gid in six.moves.xrange(ngpu)
]
else:
train_iters = [
ToggleableShufflingSerialIterator(TransformDataset(
train_subsets[gid], load_tr),
batch_size=1,
shuffle=not use_sortagrad)
for gid in six.moves.xrange(ngpu)
]
# set up updater
updater = CustomParallelUpdater(train_iters,
optimizer,
converter=converter,
devices=devices)
# Set up a trainer
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
if use_sortagrad:
trainer.extend(
ShufflingEnabler(train_iters),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
if args.opt == 'noam':
from espnet.nets.chainer_backend.transformer.training import VaswaniRule
trainer.extend(VaswaniRule('alpha',
d=args.adim,
warmup_steps=args.transformer_warmup_steps,
scale=args.transformer_lr),
trigger=(1, 'iteration'))
# Resume from a snapshot
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
# set up validation iterator
valid = make_batchset(valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
if args.n_iter_processes > 0:
valid_iter = chainer.iterators.MultiprocessIterator(
TransformDataset(valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
valid_iter = chainer.iterators.SerialIterator(TransformDataset(
valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
BaseEvaluator(valid_iter, model, converter=converter, device=gpu_id))
# 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.calculate_all_attentions
plot_class = model.module.attention_plot_class
else:
att_vis_fn = model.calculate_all_attentions
plot_class = model.attention_plot_class
logging.info('Using custom PlotAttentionReport')
att_reporter = plot_class(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
transform=load_cv,
device=gpu_id)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Take a snapshot for each specified epoch
trainer.extend(
extensions.snapshot(filename='snapshot.ep.{.updater.epoch}'),
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'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode != 'ctc':
trainer.extend(
extensions.snapshot_object(model, 'model.acc.best'),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode != 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best'),
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'),
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=(args.report_interval_iters,
'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').eps),
trigger=(args.report_interval_iters, 'iteration'))
report_keys.append('eps')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(args.report_interval_iters, 'iteration'))
trainer.extend(
extensions.ProgressBar(update_interval=args.report_interval_iters))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
writer = SummaryWriter(args.tensorboard_dir)
trainer.extend(TensorboardLogger(writer, att_reporter),
trigger=(args.report_interval_iters, 'iteration'))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)