def main():
parser = argparse.ArgumentParser(description='DCGAN for mnist')
parser.add_argument('--batchsize',
'-b',
type=int,
default=1,
help='# of each mini-batch size')
parser.add_argument('--epoch',
'-e',
type=int,
default=500,
help='# of epoch')
parser.add_argument(
'--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (if you want to use gpu, set positive value)')
parser.add_argument('--dataset',
'-d',
type=str,
default='',
help='path of training dataset path.')
parser.add_argument('--out_dir',
'-o',
type=str,
default='result',
help='path of output the result.')
parser.add_argument('--n_hidden',
'-n',
type=int,
default=100,
help='# of hidden unit(z)')
args = parser.parse_args()
logger = set_logger()
logger.debug('=' * 10)
logger.debug('GPU: {}'.format(args.gpu))
logger.debug('#batchsize: {}'.format(args.batchsize))
logger.debug('#epoch: {}'.format(args.epoch))
logger.debug('n_hidden: {}'.format(args.n_hidden))
logger.debug('dataset: {}'.format(args.dataset))
logger.debug('out_dir: {}'.format(args.out_dir))
logger.debug('=' * 10)
print()
logger.debug('setup models')
# Setup networks
generator = Generator(z_dim=args.n_hidden)
discriminator = Discriminator(z_dim=args.n_hidden)
if args.gpu >= 0:
chainer.backends.cuda.get_device_from_id(args.gpu).use()
generator.to_gpu()
discriminator.to_gpu()
# Setup optimizers
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = chainer.optimizers.Adam(alpha=alpha, beta1=beta1)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0001),
'hook_dec')
return optimizer
opt_generator = make_optimizer(generator)
opt_discriminator = make_optimizer(discriminator)
if args.dataset == '':
train, _ = chainer.datasets.get_mnist(withlabel=False,
ndim=3,
scale=255.)
else:
pass
# Setup an iterator
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
# Setup an Updater
updater = GANUpdater(models=(generator, discriminator),
iterator=train_iter,
optimizer={
'gen': opt_generator,
'dis': opt_discriminator
},
device=args.gpu)
# Setup a trainer
trainer = training.Trainer(updater, (args.epoch, 'epoch'),
out=args.out_dir)
snapshot_interval = (1000, 'iteration')
display_interval = (100, 'iteration')
trainer.extend(
extensions.snapshot(filename='snapshot_iter_{.updater.iteration}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
generator, 'generator_iter_{.updater.iteration}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
discriminator, 'discriminator_iter_{.updater.iteration}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.LogReport(trigger=snapshot_interval))
trainer.extend(extensions.PrintReport([
'epoch',
'iteration',
'gen/loss',
'dis/loss',
]),
trigger=display_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
# Start train
logger.debug('Training Start.')
print()
print()
trainer.run()
img=Image.fromarray(im)
return img
def save_img(img_array,save_path): #save from np.array (3,height,width)
img = array_to_img(img_array)
img.save(save_path)
Gen = Generator()
Dis = Discriminator()
gpu = -1
if gpu>=0:
xp = cuda.cupy
cuda.get_device(gpu).use()
Gen.to_gpu()
Dis.to_gpu()
else:
xp = np
optG = Gen.make_optimizer()
optD = Dis.make_optimizer()
optG.setup(Gen)
optD.setup(Dis)
real = RPGCharacters()
trainsize=real.train_size
testsize=real.test_size
batchsize = 64
max_epoch = 100
for epoch in range(max_epoch):