def main(argv):
(opt, args) = parser.parse_args(argv)
print(opt)
config = get_config(opt.config)
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)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
torch.cuda.set_device(opt.gpu_ids)
cudnn.benchmark = True
# loading data set
transform = transforms.Compose([transforms.Resize((config['fineSizeH'], config['fineSizeW'])),
transforms.ToTensor()])
dataset = Aligned_Dataset(config['dataPath'], subfolder='test', direction='AtoB', transform=transform)
test_loader = torch.utils.data.DataLoader(dataset, batch_size=1,
shuffle=False, num_workers=int(4))
# setup model
trainer = trainer_gan(config, test_loader, resume_epoch=opt.resume_epoch)
# load a model
trainer.netG.load_state_dict(torch.load(opt.modeldir))
if opt.cuda:
trainer.cuda()
# testing
trainer.test()
def main(argv):
(opt, args) = parser.parse_args(argv)
print(opt)
config = get_config(opt.config)
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)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
torch.cuda.set_device(opt.gpu_ids)
cudnn.benchmark = True
# loading data set
transform = transforms.Compose([transforms.Resize((config['fineSizeH'], config['fineSizeW'])),
transforms.ToTensor()])
dataset = Aligned_Dataset(config['dataPath'], direction='AtoB', transform=transform)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=config['batchSize'],
shuffle=True, num_workers=int(4))
# setup model
trainer = trainer_gan(config, train_loader, resume_epoch=opt.resume_epoch)
if opt.cuda:
trainer.cuda()
if opt.resume_epoch:
trainer.resume()
# training
for epoch in range(opt.resume_epoch, config['nepoch']):
trainer.train(epoch)
trainer.update_learning_rate(epoch)
if epoch % 10 == 0:
trainer.save(epoch)
def main(argv):
(opt, args) = parser.parse_args(argv)
print(opt)
config = get_config(opt.config)
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)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
torch.cuda.set_device(opt.gpu_ids)
cudnn.benchmark = True
# loading data set
transform = transforms.Compose([
transforms.Resize((config['fineSize'], config['fineSize'])),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
dataset = Aligned_Dataset(config['dataPath'],
subfolder='test',
direction='AtoB',
transform=transform)
test_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=True,
num_workers=int(4))
# setup model
trainer = trainer_gan(config, test_loader)
# load a model
trainer.netG.load_state_dict(torch.load(opt.modeldir))
if opt.cuda:
trainer.cuda()
# testing
trainer.test()