from become_yukarin.model.model import create
from become_yukarin.updater.updater import Updater
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,
from become_yukarin.model.model import create
from become_yukarin.updater.updater import Updater
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)