from data import CelebA_HQ
test_dataset = CelebA_HQ(args.data_path, args.attr_path, args.image_list_path, args.img_size, 'test', args.attrs)
os.makedirs(output_path, exist_ok=True)
test_dataloader = data.DataLoader(
test_dataset, batch_size=1, num_workers=args.num_workers,
shuffle=False, drop_last=False
)
if args.num_test is None:
print('Testing images:', len(test_dataset))
else:
print('Testing images:', min(len(test_dataset), args.num_test))
attgan = AttGAN(args)
attgan.load(find_model(join('output', args.experiment_name, 'checkpoint'), args.load_epoch))
progressbar = Progressbar()
attgan.eval()
for idx, (img_a, att_a) in enumerate(test_dataloader):
if args.num_test is not None and idx == args.num_test:
break
img_a = img_a.cuda() if args.gpu else img_a
att_a = att_a.cuda() if args.gpu else att_a
att_a = att_a.type(torch.float)
att_b_list = [att_a]
if args.by_levels:
for i in range(args.n_attrs):
args.attrs)
train_dataloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle=True,
drop_last=True)
valid_dataloader = data.DataLoader(valid_dataset,
batch_size=args.n_samples,
num_workers=args.num_workers,
shuffle=False,
drop_last=False)
print('Training images:', len(train_dataset), '/', 'Validating images:',
len(valid_dataset))
attgan = AttGAN(args)
progressbar = Progressbar()
writer = SummaryWriter(join('output', args.experiment_name, 'summary'))
fixed_img_a, fixed_att_a = next(iter(valid_dataloader))
fixed_img_a = fixed_img_a.cuda() if args.gpu else fixed_img_a
fixed_att_a = fixed_att_a.cuda() if args.gpu else fixed_att_a
fixed_att_a = fixed_att_a.type(torch.float)
sample_att_b_list = [fixed_att_a]
for i in range(args.n_attrs):
tmp = fixed_att_a.clone()
tmp[:, i] = 1 - tmp[:, i]
tmp = check_attribute_conflict(tmp, args.attrs[i], args.attrs)
sample_att_b_list.append(tmp)
it = 0
it_per_epoch = len(train_dataset) // args.batch_size