shuffle=True,
num_workers=int(opt.workers))
# some hyper parameters
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)
num_classes = int(opt.num_classes)
nc = 3
# Define the generator and initialize the weights
if opt.dataset == 'cifar10':
netG = _netG_CIFAR10(ngpu, nz)
else:
netG = _netG(ngpu, nz)
netG.apply(weights_init)
if opt.netG != '':
netG.load_state_dict(torch.load(opt.netG))
print(netG)
# Define the discriminator and initialize the weights
if opt.dataset == 'cifar10':
netD = _netD_CIFAR10(ngpu, num_classes)
else:
netD = _netD(ngpu, num_classes)
netD.apply(weights_init)
if opt.netD != '':
netD.load_state_dict(torch.load(opt.netD))
print(netD)
def __init__(self, params):
self.params = params
# specify the gpu id if using only 1 gpu
if params.ngpu == 1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(params.gpu_id)
try:
os.makedirs(params.outf)
except OSError:
pass
if params.manualSeed is None:
params.manualSeed = random.randint(1, 10000)
print("Random Seed: ", params.manualSeed)
random.seed(params.manualSeed)
torch.manual_seed(params.manualSeed)
if params.cuda:
torch.cuda.manual_seed_all(params.manualSeed)
cudnn.benchmark = True
if torch.cuda.is_available() and not params.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# datase t
if params.dataset == 'imagenet':
# folder dataset
self.dataset = ImageFolder(
root=params.dataroot,
transform=transforms.Compose([
transforms.Scale(params.imageSize),
transforms.CenterCrop(params.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]),
classes_idx=(10, 20))
elif params.dataset == 'cifar10':
self.dataset = dset.CIFAR10(root=params.dataroot,
download=True,
transform=transforms.Compose([
transforms.Scale(params.imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
else:
raise NotImplementedError("No such dataset {}".format(
params.dataset))
assert self.dataset
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=params.batchSize,
shuffle=True,
num_workers=int(params.workers))
# some hyper parameters
self.ngpu = int(params.ngpu)
self.nz = int(params.nz)
self.ngf = int(params.ngf)
self.ndf = int(params.ndf)
self.num_classes = int(params.num_classes)
self.nc = 3
# Define the generator and initialize the weights
if params.dataset == 'imagenet':
self.netG = _netG(self.ngpu, self.nz)
else:
self.netG = _netG_CIFAR10(self.ngpu, self.nz)
self.netG.apply(weights_init)
if params.netG != '':
self.netG.load_state_dict(torch.load(params.netG))
print(self.netG)
# Define the discriminator and initialize the weights
if params.dataset == 'imagenet':
self.netD = _netD(self.ngpu, self.num_classes)
else:
self.netD = _netD_CIFAR10(self.ngpu, self.num_classes)
self.netD.apply(weights_init)
if params.netD != '':
self.netD.load_state_dict(torch.load(params.netD))
print(self.netD)
# loss functions
self.dis_criterion = nn.BCELoss()
self.aux_criterion = nn.NLLLoss()
# tensor placeholders
self.input = torch.FloatTensor(params.batchSize, 3, params.imageSize,
params.imageSize)
self.noise = torch.FloatTensor(params.batchSize, self.nz, 1, 1)
self.eval_noise = torch.FloatTensor(params.batchSize, self.nz, 1,
1).normal_(0, 1)
self.dis_label = torch.FloatTensor(params.batchSize)
self.aux_label = torch.LongTensor(params.batchSize)
self.real_label = 1
self.fake_label = 0
# if using cuda
if params.cuda:
self.netD.cuda()
self.netG.cuda()
self.dis_criterion.cuda()
self.aux_criterion.cuda()
self.input, self.dis_label, self.aux_label = self.input.cuda(
), self.dis_label.cuda(), self.aux_label.cuda()
self.noise, self.eval_noise = self.noise.cuda(
), self.eval_noise.cuda()
# define variables
self.input = Variable(self.input)
self.noise = Variable(self.noise)
self.eval_noise = Variable(self.eval_noise)
self.dis_label = Variable(self.dis_label)
self.aux_label = Variable(self.aux_label)
# noise for evaluation
self.eval_noise_ = np.random.normal(0, 1, (params.batchSize, self.nz))
self.eval_label = np.random.randint(0, self.num_classes,
params.batchSize)
self.eval_onehot = np.zeros((params.batchSize, self.num_classes))
self.eval_onehot[np.arange(params.batchSize), self.eval_label] = 1
self.eval_noise_[np.arange(params.batchSize), :self.
num_classes] = self.eval_onehot[np.arange(
params.batchSize)]
self.eval_noise_ = (torch.from_numpy(self.eval_noise_))
self.eval_noise.data.copy_(
self.eval_noise_.view(params.batchSize, self.nz, 1, 1))
# setup optimizer
self.optimizerD = optim.Adam(self.netD.parameters(),
lr=params.lr,
betas=(params.beta1, 0.999))
self.optimizerG = optim.Adam(self.netG.parameters(),
lr=params.lr,
betas=(params.beta1, 0.999))
