def main(opt):
if type(opt.seed) is int:
torch.manual_seed(opt.seed)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
(x_train, y_train), _ = load_mnist("dataset",
training_label=opt.training_label,
split_rate=opt.split_rate)
train_mnist = SimpleDataset(x_train,
y_train,
transform=transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
]))
train_dataloader = DataLoader(train_mnist,
batch_size=opt.batch_size,
shuffle=True)
img_shape = (opt.channels, opt.img_size, opt.img_size)
generator = Generator(img_shape, opt.latent_dim)
discriminator = Discriminator(img_shape)
encoder = Encoder(img_shape)
train_encoder_izif(opt, generator, discriminator, encoder,
train_dataloader, device)
def main(opt):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
_, (x_test, y_test) = load_mnist("dataset",
training_label=opt.training_label,
split_rate=opt.split_rate)
test_mnist = SimpleDataset(x_test,
y_test,
transform=transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
]))
test_dataloader = DataLoader(test_mnist, batch_size=1, shuffle=False)
generator = Generator(opt)
discriminator = Discriminator(opt)
encoder = Encoder(opt)
test_anomaly_detection(opt, generator, discriminator, encoder,
test_dataloader, device)
def main(opt):
if type(opt.seed) is int:
torch.manual_seed(opt.seed)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
_, (x_test, y_test) = load_mnist("dataset",
training_label=opt.training_label,
split_rate=opt.split_rate)
test_mnist = SimpleDataset(x_test,
y_test,
transform=transforms.Compose([
transforms.ToPILImage(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
]))
test_dataloader = DataLoader(test_mnist,
batch_size=opt.n_grid_lines,
shuffle=True)
generator = Generator(opt)
encoder = Encoder(opt)
save_compared_images(opt, generator, encoder, test_dataloader, device)