print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
cudnn.benchmark = True
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
#########################
#### Dataset prepare ####
#########################
dataset = make_dataset(dataset=opt.dataset,
dataroot=opt.dataroot,
imageSize=opt.imageSize)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers))
#########################
#### Models building ####
#########################
device = torch.device("cuda:0" if opt.cuda else "cpu")
##device = torch.device("cpu")
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
cudnn.benchmark = True
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
#########################
#### Dataset prepare ####
#########################
dataset = make_dataset(dataset=opt.dataset, dataroot=opt.dataroot, imageSize=opt.imageSize)
assert dataset
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
shuffle=True, num_workers=int(opt.workers))
#########################
#### Models building ####
#########################
device = torch.device("cuda:0" if opt.cuda else "cpu")
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)
nc = 3
netG = Generator(ngpu).to(device)
