train_sr.py

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 chainerui.utils import save_args

from become_yukarin.config.sr_config import create_from_json
from become_yukarin.dataset import create_sr as create_sr_dataset
from become_yukarin.model.sr_model import create_sr as create_sr_model
from become_yukarin.updater.sr_updater import SRUpdater

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, discriminator = create_sr_model(config.model)
models = {
    'predictor': predictor,
    'discriminator': discriminator,
}

# dataset
dataset = create_sr_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
def create_optimizer(model):
    optimizer = optimizers.Adam(alpha=0.0002, beta1=0.5, beta2=0.999)
    optimizer.setup(model)
    return optimizer


opts = {key: create_optimizer(model) for key, model in models.items()}

# updater
converter = partial(convert.concat_examples, padding=0)
updater = SRUpdater(
    loss_config=config.loss,
    predictor=predictor,
    discriminator=discriminator,
    device=config.train.gpu,
    iterator=train_iter,
    optimizer=opts,
    converter=converter,
)

# trainer
trigger_log = (config.train.log_iteration, 'iteration')
trigger_snapshot = (config.train.snapshot_iteration, 'iteration')

trainer = training.Trainer(updater, out=arguments.output)

ext = extensions.Evaluator(test_iter, models, converter, device=config.train.gpu, eval_func=updater.forward)
trainer.extend(ext, name='test', trigger=trigger_log)
ext = extensions.Evaluator(train_eval_iter, models, converter, device=config.train.gpu, eval_func=updater.forward)
trainer.extend(ext, name='train', trigger=trigger_log)

trainer.extend(extensions.dump_graph('predictor/loss'))

ext = extensions.snapshot_object(predictor, filename='predictor_{.updater.iteration}.npz')
trainer.extend(ext, trigger=trigger_snapshot)

trainer.extend(extensions.LogReport(trigger=trigger_log))
trainer.extend(extensions.PrintReport(['predictor/loss']))

save_args(arguments, arguments.output)
trainer.run()