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 == 'test_aligned':
from data.aligned_dataset import TestAlignedDataset
dataset = TestAlignedDataset()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
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 == 'aligned_with_C':
from data.aligned_dataset_with_C import AlignedDatasetWithC
dataset = AlignedDatasetWithC()
elif opt.dataset_mode == 'aligned_multi_view':
from data.aligned_dataset_multi_view import AlignedDatasetMultiView
dataset = AlignedDatasetMultiView()
elif opt.dataset_mode == 'aligned_multi_view_random':
from data.aligned_dataset_multi_view_random import AlignedDatasetMultiView
dataset = AlignedDatasetMultiView()
elif opt.dataset_mode == 'aligned_depth':
from data.aligned_dataset_depth import AlignedDatasetDepth
dataset = AlignedDatasetDepth()
elif opt.dataset_mode == 'appearance_flow':
from data.appearance_flow_dataloader import AppearanceFlowDataloader
dataset = AppearanceFlowDataloader()
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_with_guidance':
from data.unaligned_dataset_with_guidance import UnalignedDatasetWithGuidance
dataset = UnalignedDatasetWithGuidance()
elif opt.dataset_mode == 'unaligned_with_label':
from data.unaligned_dataset_with_label import UnalignedDatasetWithLabel
dataset = UnalignedDatasetWithLabel()
elif opt.dataset_mode == 'unaligned_tensor_with_label':
from data.unaligned_tensor_dataset_with_label import UnalignedTensorDatasetWithLabel
dataset = UnalignedTensorDatasetWithLabel()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
def CreateDataset(opt):
dataset = None
# Data stored as one image concatenated along horizontal axis
if opt.dataset_mode == 'aligned':
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
# Data stored in different directories
elif opt.dataset_mode == 'unaligned':
from data.unaligned_dataset import UnalignedDataset
dataset = UnalignedDataset()
elif opt.dataset_mode == 'geo':
from data.geo_dataset import GeoDataset
dataset = GeoDataset()
elif opt.dataset_mode == 'single':
from data.single_dataset import SingleDataset
dataset = SingleDataset()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
def CreateDataset(opt):
dataset = None
#print("================="+str(opt.dataset_mode))
if opt.dataset_mode == 'aligned':
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
## add 3D videodataset loader
elif opt.dataset_mode == 'v':
from data.video_data import VideoDataset
dataset = VideoDataset()
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()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
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 == 'single':
from data.single_dataset import SingleDataset
dataset = SingleDataset()
elif opt.dataset_mode == 'unaligned_landmark':
from data.unaligned_landmark_dataset import UnalignedLandmarkDataset
dataset = UnalignedLandmarkDataset()
elif opt.dataset_mode == 'aligned_heatmap2face':
from data.aligned_dataset import AlignedDatasetHeatmap2Face
dataset = AlignedDatasetHeatmap2Face()
elif opt.dataset_mode == 'aligned_boundary_detection':
from data.aligned_dataset import AlignedBoundaryDetection
dataset = AlignedBoundaryDetection()
elif opt.dataset_mode == 'aligned_boundary_detection_landmarks':
from data.aligned_dataset import AlignedBoundaryDetectionLandmark
dataset = AlignedBoundaryDetectionLandmark()
elif opt.dataset_mode == 'aligned_face2boundary2face':
from data.aligned_dataset import AlignedFace2Boudnary2Face
dataset = AlignedFace2Boudnary2Face()
elif opt.dataset_mode == 'aligned_face2face':
from data.aligned_dataset import AlignedFace2Face
dataset = AlignedFace2Face()
elif opt.dataset_mode == 'aligned_faceDataset':
from data.aligned_dataset import AlignedFaceDataset
dataset = AlignedFaceDataset()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
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()
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)
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
def initialize(self, opt):
UnalignedDataset.initialize(self, opt)
def CreateDataset(opt):
dataset = UnalignedDataset()
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 == '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 create_data_loader(opt, phase, batch_size, shuffle, num_workers):
dataset = UnalignedDataset(opt, phase)
return torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
