train.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 become_yukarin.config.config import create_from_json
from become_yukarin.dataset import create as create_dataset
from become_yukarin.model.model import create
from become_yukarin.updater.updater import Updater

if __name__ == '__main__':
    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(config.model)
    models = {
        'predictor': predictor,
        'discriminator': discriminator,
    }

    # 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
    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 = Updater(
        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.run()