help='number of frames in an animation for the gif')
opt = parser.parse_args()
print(opt)
mnist = MNIST()
mnist_train = mnist.train_loader
if opt.cuda:
cuda_gpu = torch.device('cuda:0')
cppn = CPPN(x_dim=opt.x_dim,
y_dim=opt.y_dim,
scale=opt.scale,
cuda_device=cuda_gpu)
cppn.cuda()
cppn.load_state_dict(torch.load(opt.model))
else:
cppn = CPPN(x_dim=opt.x_dim, y_dim=opt.y_dim, scale=opt.scale)
cppn.load_state_dict(torch.load(opt.model, map_location='cpu'))
cppn.eval()
enc = []
lab = []
for idx, (im, label) in enumerate(mnist_train):
with torch.no_grad():
if opt.cuda:
im = im.cuda()
mean, logvar = cppn.encoder(im)
encoding = cppn.reparametrize(mean, logvar)
help='number of cpu threads to use during\
batch generation')
opt = parser.parse_args()
print(opt)
mnist = MNIST()
train_mnist = mnist.train_loader
test = mnist.test_loader
if opt.cuda:
cuda_gpu = torch.device('cuda:0')
model = CPPN(cuda_device=cuda_gpu)
model.cuda()
else:
model = CPPN()
le = 0
ld = 0
lg = 0
indices_train = np.arange(60000)
model.train()
for epoch in range(opt.n_epochs):
print("STARTING EPOCH {}".format(epoch))
for idx, (im, _) in enumerate(train_mnist):
model.optimizer_discriminator.zero_grad()
model.optimizer_encoder.zero_grad()