if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
if not opt.silent:
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
opt.device = "cuda:0"
device = torch.device(opt.device)
cudnn.benchmark = True
if opt.dataset == 'lfw':
trainset = datasets.LFWDataset(dataPath=opt.dataroot,
sets='train',
transform=transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST),
transforms.CenterCrop(opt.sizex),
transforms.ToTensor(),
]),)
testset = datasets.LFWDataset(dataPath=opt.dataroot,
sets='test',
transform=transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST),
transforms.CenterCrop(opt.sizex),
transforms.ToTensor(),
]),)
valset = datasets.LFWDataset(dataPath=opt.dataroot,
sets='val',
transform=transforms.Compose([transforms.Resize(opt.sizex, Image.NEAREST),
transforms.CenterCrop(opt.sizex),
transforms.ToTensor(),
]),)
if opt.dataset == 'cub':
if not load_options.statePathD is None:
states = torch.load(load_options.statePathD,
map_location={'cuda:0': load_options.device})
opt = states['options']
netDX = models._resDiscriminator128(nIn=opt.nx,
nf=opt.nfD,
selfAtt=opt.useSelfAttD).to(device)
netDX.load_state_dict(states['netDX'])
netDX.eval()
if opt.dataset == "lfw":
dataset = datasets.LFWDataset(
dataPath=load_options.dataroot,
sets='test',
transform=torchvision.transforms.Compose([
torchvision.transforms.Resize(opt.sizex, Image.NEAREST),
torchvision.transforms.CenterCrop(opt.sizex),
torchvision.transforms.ToTensor(),
]),
)
if opt.dataset == 'cub':
dataset = datasets.CUBDataset(
load_options.dataroot, "train",
torchvision.transforms.Compose([
torchvision.transforms.Resize(opt.sizex, Image.NEAREST),
torchvision.transforms.CenterCrop(opt.sizex),
torchvision.transforms.ToTensor(),
]))
if opt.dataset == 'flowers':
dataset = datasets.FlowersDataset(
load_options.dataroot, "train",