def CreateDataset(opt):
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
def CreateDataset(opt):
dataset = None
if opt.dataset_mode == 'aligned':
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
elif opt.dataset_mode == 'unaligned':
from data.unaligned_dataset import UnalignedDataset
dataset = UnalignedDataset()
elif opt.dataset_mode == 'aligned_rand':
from data.aligned_dataset_rand import AlignedDataset_Rand
dataset = AlignedDataset_Rand()
elif opt.dataset_mode == 'aligned_test':
from data.aligned_dataset_test import AlignedDataset_Test
dataset = AlignedDataset_Test()
elif opt.dataset_mode == 'unaligned_seg':
from data.unaligned_dataset_seg import UnalignedDataset_Seg
dataset = UnalignedDataset_Seg()
elif opt.dataset_mode == 'aligned_seg':
from data.aligned_dataset_seg import AlignedDataset_Seg
dataset = AlignedDataset_Seg()
elif opt.dataset_mode == 'aligned_seg_rand':
from data.aligned_dataset_seg_rand import AlignedDataset_Seg_Rand
dataset = AlignedDataset_Seg_Rand()
elif opt.dataset_mode == 'single':
from data.single_dataset import SingleDataset
dataset = SingleDataset()
elif opt.dataset_mode == 'fivek':
from data.fivek_dataset import FiveKDataset
dataset = FiveKDataset()
elif opt.dataset_mode == 'fivek2':
from data.fivek_dataset2 import FiveKDataset2
dataset = FiveKDataset2()
elif opt.dataset_mode == 'fivek3':
from data.fivek_dataset3 import FiveKDataset3
dataset = FiveKDataset3()
elif opt.dataset_mode == 'fivek4':
from data.fivek_dataset4 import FiveKDataset4
dataset = FiveKDataset4()
elif opt.dataset_mode == 'fivek4_syn':
from data.fivek_dataset4_syn import FiveKDataset4_syn
dataset = FiveKDataset4_syn()
elif opt.dataset_mode == 'fivek_single':
from data.fivek_single import FiveKDataset_single
dataset = FiveKDataset_single()
elif opt.dataset_mode == 'ava':
from data.ava_dataset import AVADataset
dataset = AVADataset()
elif opt.dataset_mode == 'aadb':
from data.aadb_dataset import AADBDataset
dataset = AADBDataset()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
opt.display_freq = 1
opt.print_freq = 1
opt.niter = 1
opt.niter_decay = 0
opt.max_dataset_size = 10
n_gpu = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
opt.distributed = n_gpu > 1
local_rank = opt.local_rank
if opt.distributed:
torch.cuda.set_device(opt.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
synchronize()
dataset = AlignedDataset()
dataset.initialize(opt)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=opt.batchSize,
shuffle=False,
num_workers=int(opt.workers))
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = (start_epoch - 1) * dataset_size + epoch_iter
display_delta = total_steps % opt.display_freq
print_delta = total_steps % opt.print_freq
save_delta = total_steps % opt.save_latest_freq
def CreateDataset(opt):
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
dataset.initialize(opt)
return dataset
def CreateDataset_stage1(opt):
dataset = None
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
dataset.initialize(opt)
return dataset
def CreateDataset(opt):
dataset = None
if opt.dataset_mode == 'aligned':
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
elif opt.dataset_mode == 'unaligned':
from data.unaligned_dataset import UnalignedDataset
dataset = UnalignedDataset()
elif opt.dataset_mode == 'single':
from data.single_dataset import SingleDataset
dataset = SingleDataset()
elif opt.dataset_mode == 'unaligned_A_labeled':
from data.unaligned_A_labeled_dataset import UnalignedALabeledDataset
dataset = UnalignedALabeledDataset()
elif opt.dataset_mode == 'mnist_svhn':
from data.mnist_svhn_dataset import MnistSvhnDataset
dataset = MnistSvhnDataset()
elif opt.dataset_mode == 'mnist_mnistfg':
from data.mnist_mnistfg_dataset import MnistMnistfgDataset
dataset = MnistMnistfgDataset()
elif opt.dataset_mode == 'mnistfg_test':
from data.mnistfg_test_dataset import MnistfgTestDataset
dataset = MnistfgTestDataset()
elif opt.dataset_mode == 'cifar10_cifar10fg':
from data.cifar10_cifar10fg_dataset import Cifar10Cifar10fgDataset
dataset = Cifar10Cifar10fgDataset()
elif opt.dataset_mode == 'cifar10fg_test':
from data.cifar10fg_test_dataset import Cifar10fgTestDataset
dataset = Cifar10fgTestDataset()
elif opt.dataset_mode == 'cifar10_cifar10bim':
from data.cifar10_cifar10bim_dataset import Cifar10Cifar10bimDataset
dataset = Cifar10Cifar10bimDataset()
elif opt.dataset_mode == 'cifar10bim_test':
from data.cifar10bim_test_dataset import Cifar10bimTestDataset
dataset = Cifar10bimTestDataset()
elif opt.dataset_mode == 'cifar10_cifar10df':
from data.cifar10_cifar10df_dataset import Cifar10Cifar10dfDataset
dataset = Cifar10Cifar10dfDataset()
elif opt.dataset_mode == 'cifar10df_test':
from data.cifar10df_test_dataset import Cifar10dfTestDataset
dataset = Cifar10dfTestDataset()
elif opt.dataset_mode == 'mnist_mnistdf':
from data.mnist_mnistdf_dataset import MnistMnistdfDataset
dataset = MnistMnistdfDataset()
elif opt.dataset_mode == 'mnistdf_test':
from data.mnistdf_test_dataset import MnistdfTestDataset
dataset = MnistdfTestDataset()
elif opt.dataset_mode == 'mnist_mnistbim':
from data.mnist_mnistbim_dataset import MnistMnistbimDataset
dataset = MnistMnistbimDataset()
elif opt.dataset_mode == 'mnistbim_test':
from data.mnistbim_test_dataset import MnistbimTestDataset
dataset = MnistbimTestDataset()
elif opt.dataset_mode == 'svhn_mnist':
from data.svhn_mnist_dataset import SvhnMnistDataset
dataset = SvhnMnistDataset()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
def test_dataloader(self):
dataset = AlignedDataset(self.dataroot, 'test', self.load_size,
self.crop_size, self.preprocess)
return DataLoader(dataset, batch_size=1, num_workers=4)
