celeb_path = os.path.join(args.data_path, "celebA")
cifar_path = os.path.join(args.data_path, "cifar10")
cifar100_path = os.path.join(args.data_path, "cifar100")
svhn_path = os.path.join(args.data_path, "svhn")
mnist_path = os.path.join(
args.data_path, "mnist") # can leave empty, data will self-populate
imagenet_path = os.path.join(args.data_path, args.dataset)
if args.dataset == "mnist":
dataset = MnistDataset(mnist_path)
elif args.dataset == "celeb":
dataset = CelebDataset(celeb_path)
elif args.dataset == "cifar100":
dataset = Cifar100(cifar100_path)
dataset_get_variance = Cifar100(cifar100_path)
elif args.dataset == "cifar":
dataset = Cifar10(cifar_path)
dataset_get_variance = Cifar10(cifar_path)
elif args.dataset == "svhn":
dataset = SVHN(svhn_path)
dataset_get_variance = SVHN(svhn_path)
elif "imagenet" in args.dataset:
num_classes = int(args.dataset.split("_")[-1])
dataset = ImageNet(imagenet_path, num_classes)
else:
raise RuntimeError("invalid dataset %s" % args.dataset)
### main call
b_dcgan(dataset, dataset_get_variance, args)
import pprint
with open(os.path.join(args.out_dir, "hypers.txt"), "w") as hf:
hf.write("Hyper settings:\n")
hf.write("%s\n" % (pprint.pformat(args.__dict__)))
celeb_path = os.path.join(args.data_path, "celebA")
cifar_path = os.path.join(args.data_path, "cifar-10-batches-py")
svhn_path = os.path.join(args.data_path, "svhn")
mnist_path = os.path.join(
args.data_path, "mnist") # can leave empty, data will self-populate
imagenet_path = os.path.join(args.data_path, args.dataset)
#imagenet_path = os.path.join(args.data_path, "imagenet")
if args.dataset == "mnist":
dataset = MnistDataset(mnist_path)
elif args.dataset == "celeb":
dataset = CelebDataset(celeb_path)
elif args.dataset == "cifar":
dataset = Cifar10(cifar_path)
elif args.dataset == "svhn":
dataset = SVHN(svhn_path)
elif "imagenet" in args.dataset:
num_classes = int(args.dataset.split("_")[-1])
dataset = ImageNet(imagenet_path, num_classes)
else:
raise RuntimeError("invalid dataset %s" % args.dataset)
### main call
b_dcgan(dataset, args)
celeb_path = os.path.join(args.data_path, "celebA")
cifar_path = os.path.join(args.data_path, "cifar-10-batches-py")
svhn_path = os.path.join(args.data_path, "svhn")
mnist_path = os.path.join(
args.data_path, "mnist") # can leave empty, data will self-populate
imagenet_path = os.path.join(args.data_path, args.dataset)
#imagenet_path = os.path.join(args.data_path, "imagenet")
if args.dataset == "mnist":
dataset = MnistDataset(mnist_path)
elif args.dataset == "celeb":
dataset = CelebDataset(celeb_path)
elif args.dataset == "cifar":
dataset = Cifar10(cifar_path)
elif args.dataset == "svhn":
dataset = SVHN(svhn_path)
elif "imagenet" in args.dataset:
num_classes = int(args.dataset.split("_")[-1])
dataset = ImageNet(imagenet_path, num_classes)
else:
raise RuntimeError("invalid dataset %s" % args.dataset)
### main call
if args.ml_ensemble:
from ml_dcgan import ml_dcgan
root = args.out_dir
for ens in range(args.ml_ensemble):
dataset = SVHN(svhn_path, subsample=0.8)
args.out_dir = os.path.join(root, "%i" % ens)
os.makedirs(args.out_dir)
ml_dcgan(dataset, args)
