opt = TestOptions().parse()
# For testing the neural networks, manually edit/add options below
opt.gan_mode = 'none' # 'wgangp', 'lsgan', 'vanilla', 'none'
opt.n_downsample = 2 # Downsample times
opt.n_blocks = 2 # Numebr of residual blocks
opt.first_kernel = 5 # The filter size of the first convolutional layer in encoder
# Set the input dataset
opt.dataset_mode = 'CIFAR10' # Current dataset: CIFAR10, CelebA
# Set up the training procedure
opt.batchSize = 1 # batch size
opt.activation = 'sigmoid' # The output activation function at the last layer in the decoder
opt.norm_EG = 'batch'
if opt.dataset_mode == 'CIFAR10':
opt.dataroot = './data'
opt.size = 32
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
dataset = torch.utils.data.DataLoader(testset,
# For testing the neural networks, manually edit/add options below
opt.gan_mode = 'none' # 'wgangp', 'lsgan', 'vanilla', 'none'
opt.C_channel = 16 # The output channel number of encoder (Important: it controls the rate)
opt.n_downsample= 2 # Downsample times
opt.n_blocks = 2 # Numebr of residual blocks
opt.first_kernel = 5 # The filter size of the first convolutional layer in encoder
# Set the input dataset
opt.dataset_mode = 'CIFAR10' # Current dataset: CIFAR10, CelebA
# Set up the training procedure
opt.batchSize = 1 # batch size
opt.activation = 'tanh' # The output activation function at the last layer in the decoder
opt.norm_EG = 'batch'
if opt.dataset_mode == 'CIFAR10':
opt.dataroot='./data'
opt.size = 32
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
dataset = torch.utils.data.DataLoader(testset, batch_size=opt.batchSize,
shuffle=False, num_workers=2)
dataset_size = len(dataset)
print('#training images = %d' % dataset_size)
