test_dataset = CelebA(args.data_path, args.attr_path, args.img_size, 'test', args.attrs)
if args.data == 'CelebA-HQ':
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]
from os.path import join
from attgan import AttGAN
def parse(args=None):
parser = argparse.ArgumentParser()
parser.add_argument('--experiment_name',
dest='experiment_name',
type=str,
default='06-36AM on March 09, 2021')
parser.add_argument('--load_epoch', dest='load_epoch', type=int, default=0)
parser.add_argument('--gpu', dest='gpu', type=bool, default=False)
return parser.parse_args(args)
args_ = parse()
with open(join('output', args_.experiment_name, 'setting.txt'), 'r') as f:
args = json.load(f, object_hook=lambda d: argparse.Namespace(**d))
args.gpu = args_.gpu
args.experiment_name = args_.experiment_name
args.load_epoch = args_.load_epoch
args.betas = (args.beta1, args.beta2)
model = AttGAN(args)
model.load(
os.path.join('output', args.experiment_name, 'checkpoint',
'weights.' + str(args.load_epoch) + '.pth'))
model.saveG_D(os.path.join('output', args.experiment_name, 'checkpoint',
'weights_unzip.{:d}.pth'.format(args.load_epoch)),
flag='unzip')
args.image_list_path, args.img_size, 'valid',
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
args.attr_path = args_.attr_path
test_dataset = CelebA(args.data_path, args.attr_path, args.img_size, 'mytest',
args.attrs)
test_dataloader = data.DataLoader(test_dataset,
batch_size=1,
num_workers=args.num_workers,
shuffle=False,
drop_last=False)
print('Testing images:', len(test_dataset))
output_path = join('output', args.experiment_name, 'attention_testing')
os.makedirs(output_path, exist_ok=True)
attgan = AttGAN(args)
attgan.load(r'weights_unzip.17.pth')
attgan.eval()
for idx, (img_real, att_org) in enumerate(test_dataloader):
img_real = img_real.cuda() if args.gpu else img_real
att_org = att_org.cuda() if args.gpu else att_org
att_org = att_org.type(torch.float)
_, mc, mw, mh = img_real.shape
att_list = [att_org]
img_unit = img_real.view(3, mw, mh)
img_unit = ((img_unit * 0.5) + 0.5) * 255
img_unit = np.uint8(img_unit)
img_unit = img_unit[::-1, :, :].transpose(1, 2, 0)
for i in range(args.n_attrs):
tmp = att_org.clone()
dest='load_epoch',
type=str,
default='latest')
parser.add_argument('--gpu', action='store_true')
return parser.parse_args(args)
args_ = parse()
with open(join('output', args_.experiment_name, 'setting.txt'), 'r') as f:
args = json.load(f, object_hook=lambda d: argparse.Namespace(**d))
args.load_epoch = args_.load_epoch
args.gpu = args_.gpu
attgan = AttGAN(args)
attgan.load(
find_model(join('output', args.experiment_name, 'checkpoint'),
args.load_epoch))
attgan.eval()
progressbar = Progressbar()
tf = transforms.Compose([
transforms.CenterCrop(170),
transforms.Resize(args.img_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
att_list = open(args_.attr_path, 'r', encoding='utf-8').readlines()[0].split()
args.image_list_path, args.img_size, 'valid',
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)
if args.is_resume:
attgan.load(
os.path.join('output', args.experiment_name, 'checkpoint',
'weights.' + str(args.load_epoch) + '.pth'))
ea = event_accumulator.EventAccumulator(
join('output', args.experiment_name, 'summary', args.event_name))
ea.Reload()
d_loss = ea.scalars.Items('D/d_loss')
it = d_loss[-1][1]
else:
it = 0
cudnn.benchmark = True
progressbar = Progressbar()
# writer = SummaryWriter(join('output', args.experiment_name, 'summary')) if not args.is_resume else \
# SummaryWriter(join('output', args.experiment_name, 'summary', args.writer_name))
#from data import CelebA
#test_dataset = CelebA(args.data_path, args.attr_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()
att_b_ = optimumRandom(isMale)
att_b_reverse = optimumRandom(not isMale)
att_b_ = allRandom()
att_b_reverse = att_b_ * -1
#att_b_ = spesificParams()
#att_b_ = singleAttArray(12, -1)
goodArr = makeGoodAttr(isMale)
) # 建立生成图像的路径 output/128_shortcut1_inject1_none/sample_testing
# 每次处理一张图像
test_dataloader = data.DataLoader(test_dataset,
batch_size=1,
num_workers=args.num_workers,
shuffle=False,
drop_last=False)
# 如果没有限定处理多少张，那就把整个数据集都做迁移
if args.num_test is None:
print('Testing images:', len(test_dataset))
else:
print('Testing images:', min(len(test_dataset), args.num_test))
# 载入AttGAN模型
attgan = AttGAN(args)
# 载入指定节点
attgan.load(
find_model(join('output', args.experiment_name, 'checkpoint'),
args.load_epoch)
) # 载入指定节点 output/128_shortcut1_inject1_none/checkpoint/
progressbar = Progressbar()
# 进行验证
attgan.eval()
# 对图片的大循环
for idx, (img_a, att_a) in enumerate(test_dataloader):
'''
idx: 图像的索引
img_a: 图像
att_a: 标签
原始标签 label_a