def prepare_db(opt): print("Use %s dataset" % (opt.dataset)) if opt.dataset == 'mnist': train_dataset = torchvision.datasets.MNIST('./data/MNIST', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.1307,), (0.3081,)) ])) eval_dataset = torchvision.datasets.MNIST('./data/MNIST', train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.1307,), (0.3081,)) ])) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'adult': train_dataset = dataset.UCIAdult('./data/uci_adult', train=True) eval_dataset = dataset.UCIAdult('./data/uci_adult', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'letter': train_dataset = dataset.UCILetter('./data/uci_letter', train=True) eval_dataset = dataset.UCILetter('./data/uci_letter', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'yeast': train_dataset = dataset.UCIYeast('./data/uci_yeast', train=True) eval_dataset = dataset.UCIYeast('./data/uci_yeast', train=False) return {'train': train_dataset, 'eval': eval_dataset} else: raise NotImplementedError
def prepare_db(opt): print("Use %s dataset" % (opt.dataset)) if opt.dataset == 'mnist': train_dataset = torchvision.datasets.MNIST( './data/mnist', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.1307, ), (0.3081, )) ])) eval_dataset = torchvision.datasets.MNIST( './data/mnist', train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.1307, ), (0.3081, )) ])) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'adult': train_dataset = dataset.UCIAdult('./data/uci_adult', train=True) eval_dataset = dataset.UCIAdult('./data/uci_adult', train=False) #print(train_dataset) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'letter': train_dataset = dataset.UCILetter('./data/uci_letter', train=True) eval_dataset = dataset.UCILetter('./data/uci_letter', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'yeast': train_dataset = dataset.UCIYeast('./data/uci_yeast', train=True) eval_dataset = dataset.UCIYeast('./data/uci_yeast', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'credit': train_dataset = dataset.UCICredit( '/home/ubuntu/amberhsia/Neural-Decision-Forests-master/data/q4credit.csv', train=True) eval_dataset = dataset.UCICredit( '/home/ubuntu/amberhsia/Neural-Decision-Forests-master/data/q4credit.csv', train=False) return {'train': train_dataset, 'eval': eval_dataset} else: raise NotImplementedError
def prepare_db(opt): print("Use %s dataset" % (opt.dataset)) if opt.dataset == 'mnist': train_dataset = torchvision.datasets.MNIST('./data/mnist', train=True, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.1307,), (0.3081,)) ])) eval_dataset = torchvision.datasets.MNIST('./data/mnist', train=False, download=True, transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.1307,), (0.3081,)) ])) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'adult': train_dataset = dataset.UCIAdult('./data/uci_adult', train=True) eval_dataset = dataset.UCIAdult('./data/uci_adult', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'letter': train_dataset = dataset.UCILetter('./data/uci_letter', train=True) eval_dataset = dataset.UCILetter('./data/uci_letter', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'yeast': train_dataset = dataset.UCIYeast('./data/uci_yeast', train=True) eval_dataset = dataset.UCIYeast('./data/uci_yeast', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'gisette': train_dataset = dataset.UCIGisette('./data/uci_gisette', train=True) eval_dataset = dataset.UCIGisette('./data/uci_gisette', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'arrhythmia': train_dataset = dataset.UCIArrhythmia('./data/uci_arrhythmia', train=True) eval_dataset = dataset.UCIArrhythmia('./data/uci_arrhythmia', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'cardiotocography': train_dataset = dataset.UCICardiotocography('./data/uci_card', train=True) eval_dataset = dataset.UCICardiotocography('./data/uci_card', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'breastcancer': train_dataset = dataset.UCIBreastcancer('./data/uci_breast', train=True) eval_dataset = dataset.UCIBreastcancer('./data/uci_breast', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'nomao': train_dataset = dataset.UCINomao('./data/uci_nomao', train=True) eval_dataset = dataset.UCINomao('./data/uci_nomao', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'multiplefeatures': train_dataset = dataset.UCIMultiplefeatures('./data/uci_multiple_features', train=True) eval_dataset = dataset.UCIMultiplefeatures('./data/uci_multiple_features', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'madelon': train_dataset = dataset.UCIMadelon('./data/uci_madelon', train=True) eval_dataset = dataset.UCIMadelon('./data/uci_madelon', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'secom': train_dataset = dataset.UCISecom('./data/uci_secom', train=True) eval_dataset = dataset.UCISecom('./data/uci_secom', train=False) return {'train': train_dataset, 'eval': eval_dataset} elif opt.dataset == 'isolet5': train_dataset = dataset.UCIIsolet5('./data/uci_isolet5', train=True) eval_dataset = dataset.UCIIsolet5('./data/uci_isolet5', train=False) return {'train': train_dataset, 'eval': eval_dataset} else: raise NotImplementedError