def main():
# Parsing arguments
parser = argparse.ArgumentParser(description='Training GANs or VAEs')
parser.add_argument('--model', type=str, required=True)
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--epoch', type=int, default=200)
parser.add_argument('--batchsize', type=int, default=50)
parser.add_argument('--datasize', type=int, default=-1)
parser.add_argument('--output', default='output')
parser.add_argument('--zdims', type=int, default=256)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--testmode', action='store_true')
args = parser.parse_args()
# Select GPU
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Make output direcotiry if not exists
if not os.path.isdir(args.output):
os.mkdir(args.output)
# Load datasets
if args.dataset == 'mnist':
datasets = mnist.load_data()
elif args.dataset == 'svhn':
datasets = svhn.load_data()
elif args.dataset == 'hair':
datasets = hairdata.load_data()
elif args.dataset == 'hair_4tags':
datasets = hairdata_4tags.load_data()
else:
datasets = load_data(args.dataset)
print('aa')
# Construct model
if args.model not in models:
raise Exception('Unknown model:', args.model)
model = models[args.model](input_shape=datasets.images.shape[1:],
num_attrs=len(datasets.attr_names),
z_dims=args.zdims,
output=args.output)
if args.resume is not None:
model.load_model(args.resume)
# Training loop
datasets.images = datasets.images * 2.0 - 1.0
samples = np.random.normal(size=(10, args.zdims)).astype(np.float32)
model.main_loop(datasets,
samples,
datasets.attr_names,
epochs=args.epoch,
batchsize=args.batchsize,
reporter=[
'loss', 'g_loss', 'd_loss', 'g_acc', 'd_acc', 'c_loss',
'ae_loss'
])
def main(_):
# Parsing arguments
parser = argparse.ArgumentParser(description='Training GANs or VAEs')
parser.add_argument('--model', type=str, required=True)
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--datasize', type=int, default=-1)
parser.add_argument('--epoch', type=int, default=200)
parser.add_argument('--batchsize', type=int, default=50)
parser.add_argument('--output', default='output')
parser.add_argument('--zdims', type=int, default=256)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--testmode', action='store_true')
args = parser.parse_args()
# select gpu
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Make output direcotiry if not exists
if not os.path.isdir(args.output):
os.mkdir(args.output)
# Load datasets
if args.dataset == 'mnist':
datasets = mnist.load_data()
elif args.dataset == 'svhn':
datasets = svhn.load_data()
else:
#datasets = load_data(args.dataset, args.datasize)
datasets = load_images(args.dataset) # load
# Construct model
if args.model not in models:
raise Exception('Unknown model:', args.model)
#--------------------------------------------------------
print("input_shape", datasets.shape[1:])
#--------------------------------------------------------
model = models[args.model](batchsize=args.batchsize,
input_shape=datasets.shape[1:],
attr_names=None or datasets.attr_names,
z_dims=args.zdims,
output=args.output,
resume=args.resume)
if args.testmode:
model.test_mode = True
tf.set_random_seed(12345)
# Training loop
datasets.images = datasets.images.astype('float32') * 2.0 - 1.0
#--------------------------
print("datasets.images", datasets.images.shape)
model.main_loop(datasets, epochs=args.epoch)
def load_dataset(dataset_name):
if dataset_name == 'mnist':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
)
elif dataset_name == 'mnist-original':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
original=True)
elif dataset_name == 'mnist-rgb':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
use_rgb=True)
elif dataset_name == 'svhn':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = svhn.load_data(
)
elif dataset_name == 'svhn-extra':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = svhn.load_data(
include_extra=True)
elif dataset_name == 'mnist-svhn':
anchor = load_dataset('mnist-rgb')
mirror = load_dataset('svhn')
dataset = CrossDomainDatasets(dataset_name.replace('-', ''), anchor,
mirror)
elif dataset_name == 'cifar10':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = cifar10.load_data(
)
elif dataset_name == 'moving-mnist':
data = moving_mnist.load_data()
dataset = TimeCorelatedDataset(dataset_name.replace('-', ''), data)
elif dataset_name == 'lsun-bedroom':
datapath = lsun.load_data()
dataset = LargeDataset(datapath)
elif dataset_name == 'celeba':
datapath = celeba.load_data()
dataset = LargeDataset(datapath)
else:
raise KeyError("Dataset not implemented")
return dataset
def load_dataset(dataset_name):
if dataset_name == 'mnist':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
)
if dataset_name == 'stacked-mnist':
dataset = StackedDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
)
elif dataset_name == 'mnist-original':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
original=True)
elif dataset_name == 'mnist-rgb':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = mnist.load_data(
use_rgb=True)
elif dataset_name == 'svhn':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = svhn.load_data(
)
elif dataset_name == 'svhn-extra':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = svhn.load_data(
include_extra=True)
elif dataset_name == 'mnist-svhn':
anchor = load_dataset('mnist-rgb')
mirror = load_dataset('svhn')
dataset = CrossDomainDatasets(dataset_name.replace('-', ''), anchor,
mirror)
elif dataset_name == 'cifar10':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.x_test, dataset.y_test, dataset.attr_names = cifar10.load_data(
)
elif dataset_name == 'moving-mnist':
data = moving_mnist.load_data()
dataset = TimeCorelatedDataset(dataset_name.replace('-', ''), data)
elif dataset_name == 'lsun-bedroom':
datapath = lsun.load_data()
dataset = LargeDataset(datapath)
elif dataset_name == 'celeba':
datapath = celeba.load_data()
dataset = LargeDataset(datapath, buffer_size=20000)
elif dataset_name == 'celeba-128':
datapath = celeba.load_data(image_size=128)
dataset = LargeDataset(datapath, buffer_size=5000)
elif dataset_name == 'celeba-crop-128':
datapath = celeba.load_data(image_size=128, center_crop=True)
dataset = LargeDataset(datapath, buffer_size=5000)
elif dataset_name == 'celeba-crop-64':
datapath = celeba.load_data(image_size=64, center_crop=True)
dataset = LargeDataset(datapath, buffer_size=20000)
elif dataset_name == 'synthetic-8ring':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.attr_names = mixture.load_data(
type="ring", n=8, std=.05, r=1, density=5000)
elif dataset_name == 'synthetic-25grid':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.attr_names = mixture.load_data(
type="grid", n=25, std=.05, density=2500)
elif dataset_name == 'synthetic-high-dim':
dataset = ConditionalDataset(name=dataset_name.replace('-', ''))
dataset.images, dataset.attrs, dataset.attr_names = mixture.load_data(
type="high-dim", n=10, d=1200, std=1., density=5000)
elif dataset_name == 'mnist-anomaly':
x, y, x_t, y_t, y_names = mnist.load_data()
dataset = SimulatedAnomalyDetectionDataset(dataset_name.replace(
'-', ''),
x,
y,
anomaly_class=0,
test_set=(x_t, y_t))
elif dataset_name == 'svhn-anomaly':
x, y, x_t, y_t, y_names = svhn.load_data()
dataset = SimulatedAnomalyDetectionDataset(dataset_name.replace(
'-', ''),
x,
y,
anomaly_class=0,
test_set=(x_t, y_t))
elif dataset_name == 'cifar10-anomaly':
x, y, x_t, y_t, y_names = cifar10.load_data()
dataset = SimulatedAnomalyDetectionDataset(dataset_name.replace(
'-', ''),
x,
y,
anomaly_class=0,
test_set=(x_t, y_t))
else:
raise KeyError("Dataset not implemented")
return dataset