def CreateDataset(opt): dataset = None 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.model == 'pix2pixHD': from data.aligned_dataset import AlignedDataset dataset = AlignedDataset() elif opt.model == 'pix2pixHDts': if opt.face: from .aligned_paired_dataset import AlignedPairedFaceDataset dataset = AlignedPairedFaceDataset() else: from .aligned_paired_dataset import AlignedPairedDataset dataset = AlignedPairedDataset() 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() 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 # 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
class TwoAlignedDataset: def initialize(self, opt): assert opt.isTrain == True # set different phases (folders of image) opt1 = opt opt1.phase = opt.phase1 opt1.dataset_model = 'aligned' self.dataset1 = AlignedDataset() self.dataset1.initialize(opt1) opt2 = opt opt2.phase = opt.phase2 opt2.dataset_model = 'aligned' self.dataset2 = AlignedDataset() self.dataset2.initialize(opt2) def __getitem__(self, index): # make crop and flip same in two datasets w = self.dataset1.opt.loadSize h = self.dataset1.opt.loadSize w_offset = random.randint(0, max(0, w - self.dataset1.opt.fineSize - 1)) h_offset = random.randint(0, max(0, h - self.dataset1.opt.fineSize - 1)) is_flip = random.random() < 0.5 item1 = self.dataset1.get_item(index, w_offset, h_offset, is_flip) item2 = self.dataset2.get_item(index, w_offset, h_offset, is_flip) #item1 = self.dataset1[index] #item2 = self.dataset2[index] return {'dataset1_input': item1, 'dataset2_input': item2} def __len__(self): assert (len(self.dataset1) == len(self.dataset2)) return len(self.dataset1) def name(self): return 'TwoAlignedDataset'
def CreateDataset(opt): from data.aligned_dataset import AlignedDataset dataset = AlignedDataset() 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 modify_commandline_options(parser, is_train=True): AlignedDataset.modify_commandline_options(parser=parser, is_train=is_train) parser.add_argument('--unaligned_dir', help='Unaligned dir') return parser
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 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 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)
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() 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 CreateDataset(opt): from data.aligned_dataset import AlignedDataset train_dataset = AlignedDataset() train_dataset.initialize(opt) print("dataset [%s] was created" % (train_dataset.name())) test_dataset = AlignedDataset() opt_test = copy.deepcopy(opt) opt_test.shuffle = False opt_test.phase = "test" opt_test.no_flip = True opt_test.num_input_views = 9 test_dataset.initialize(opt_test) print("dataset [%s] was created" % (test_dataset.name())) validation_dataset = AlignedDataset() opt_val = copy.deepcopy(opt) opt_val.phase = "validation" opt_val.no_flip = True opt_val.shuffle = False opt_val.num_input_views = 9 validation_dataset.initialize(opt_val) print("dataset [%s] was created" % (validation_dataset.name())) return train_dataset, test_dataset, validation_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 == 'tif': from data.tif_dataset import TifDataset dataset = TifDataset(opt) elif opt.dataset_mode == 'mb': from data.mb_dataset import MBDataset dataset = MBDataset(opt) elif opt.dataset_mode == 'png_withlist': from data.png_dataset_withlist import PngDataset dataset = PngDataset(opt) elif opt.dataset_mode == 'png': from data.png_dataset import PngDataset dataset = PngDataset(opt) 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 == 'imagelist': from data.imagelist_dataset import ImageList if opt.isTrain: dataset = ImageList(root=opt.image_root, fileList=opt.train_list) else: dataset = ImageList(root=opt.image_root, fileList=opt.train_list, testPahse=True) elif opt.dataset_mode == 'imagelist_cross_view': from data.imagelist_dataset import ImageList_cross_view dataset = ImageList_cross_view() elif opt.dataset_mode == 'imglist_pts': from data.imagelist_pts_dataset import Imglist_Pts_Dataset dataset = Imglist_Pts_Dataset() else: raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode) print("dataset [%s] was created" % (dataset.name())) dataset.initialize(opt) return dataset