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import argparse
from functools import partial
from pathlib import Path
from chainer import cuda
from chainer import optimizers
from chainer import training
from chainer.dataset import convert
from chainer.iterators import MultiprocessIterator
from chainer.training import extensions
from become_yukarin.config import create_from_json
from become_yukarin.dataset import create as create_dataset
from become_yukarin.loss import Loss
from become_yukarin.model import create
parser = argparse.ArgumentParser()
parser.add_argument('config_json_path', type=Path)
parser.add_argument('output', type=Path)
arguments = parser.parse_args()
config = create_from_json(arguments.config_json_path)
arguments.output.mkdir(exist_ok=True)
config.save_as_json((arguments.output / 'config.json').absolute())
# model
if config.train.gpu >= 0:
cuda.get_device_from_id(config.train.gpu).use()
predictor, aligner = create(config.model)
model = Loss(config.loss, predictor=predictor, aligner=aligner)
# dataset
dataset = create_dataset(config.dataset)
train_iter = MultiprocessIterator(dataset['train'], config.train.batchsize)
test_iter = MultiprocessIterator(dataset['test'], config.train.batchsize, repeat=False, shuffle=False)
train_eval_iter = MultiprocessIterator(dataset['train_eval'], config.train.batchsize, repeat=False, shuffle=False)
# optimizer
optimizer = optimizers.Adam()
optimizer.setup(model)
# trainer
trigger_log = (config.train.log_iteration, 'iteration')
trigger_snapshot = (config.train.snapshot_iteration, 'iteration')
converter = partial(convert.concat_examples, padding=0)
updater = training.StandardUpdater(train_iter, optimizer, device=config.train.gpu, converter=converter)
trainer = training.Trainer(updater, out=arguments.output)
ext = extensions.Evaluator(test_iter, model, converter, device=config.train.gpu)
trainer.extend(ext, name='test', trigger=trigger_log)
ext = extensions.Evaluator(train_eval_iter, model, converter, device=config.train.gpu)
trainer.extend(ext, name='train', trigger=trigger_log)
trainer.extend(extensions.dump_graph('main/loss', out_name='graph.dot'))
ext = extensions.snapshot_object(predictor, filename='predictor_{.updater.iteration}.npz')
trainer.extend(ext, trigger=trigger_snapshot)
trainer.extend(extensions.LogReport(trigger=trigger_log, log_name='log.txt'))
if extensions.PlotReport.available():
trainer.extend(extensions.PlotReport(
y_keys=['main/loss', 'test/main/loss', 'train/main/loss'],
x_key='iteration',
file_name='loss.png',
trigger=trigger_log,
))
trainer.run()
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