def get_setting(args): kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} path = os.path.join(args.data_folder) if args.dataset == 'mnist': num_class = 10 train_loader = torch.utils.data.DataLoader(datasets.MNIST( path, train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST(path, train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'smallNORB': num_class = 5 train_loader = torch.utils.data.DataLoader(smallNORB( path, train=True, download=True, transform=transforms.Compose([ transforms.Resize(48), transforms.RandomCrop(32), transforms.ColorJitter(brightness=32. / 255, contrast=0.5), transforms.ToTensor() ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( smallNORB(path, train=False, transform=transforms.Compose([ transforms.Resize(48), transforms.CenterCrop(32), transforms.ToTensor() ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) else: raise NameError('Undefined dataset {}'.format(args.dataset)) return num_class, train_loader, test_loader
def get_setting(args): kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} path = os.path.join(args.data_folder, args.dataset) if args.dataset == 'mnist': num_class = 10 train_loader = torch.utils.data.DataLoader(datasets.MNIST( path, train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST(path, train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'smallNORB': num_class = 5 train_loader = torch.utils.data.DataLoader(smallNORB( path, train=True, download=True, transform=transforms.Compose([ transforms.Resize(48), transforms.RandomCrop(32), transforms.ColorJitter(brightness=32. / 255, contrast=0.5), transforms.ToTensor() ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( smallNORB(path, train=False, transform=transforms.Compose([ transforms.Resize(48), transforms.CenterCrop(32), transforms.ToTensor() ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'gtrsb': num_class = 43 full_dataset = GTRSB(path, download=True, transform=transforms.Compose([ transforms.Grayscale(), transforms.Resize( (48, 48), interpolation=Image.LANCZOS), transforms.ToTensor() ])) train_size = 39209 val_size = 12630 print(f"Train Size: {str(train_size)}") print(f"Val Size: {str(val_size)}") train_dataset, val_dataset = torch.utils.data.random_split( full_dataset, [train_size, val_size]) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=args.test_batch_size, shuffle=True, **kwargs) else: raise NameError('Undefined dataset {}'.format(args.dataset)) return num_class, train_loader, val_loader
logger = util.statNothing() elif args.dataset[:5] == 'MNIST': train_dataset = datasets.MNIST(root='../../data/', train=True, transform=transforms.ToTensor(), download=True) test_dataset = datasets.MNIST(root='../../data/', train=False, transform=transforms.ToTensor()) logger = util.statClassification(args) elif args.dataset == 'smallNORB': # transforms.Resize(48), train_dataset = smallNORB('../../data/smallnorb/', train=True, download=True, transform=transforms.Compose([ transforms.RandomCrop(64), transforms.ColorJitter(brightness=32. / 255, contrast=0.5), transforms.ToTensor() ])) test_dataset = smallNORB('../../data/smallnorb/', train=False, transform=transforms.Compose([ transforms.CenterCrop(64), transforms.ToTensor() ])) elif args.dataset == 'msra': train_dataset = MARAHandDataset('../../data/cvpr15_MSRAHandGestureDB', 'train', 2) #train_dataset.test() test_dataset = MARAHandDataset('../../data/cvpr15_MSRAHandGestureDB',
def get_setting(args): kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} path = os.path.join(args.data_folder, args.dataset) if args.dataset == 'mnist': num_class = 10 train_loader = torch.utils.data.DataLoader( datasets.MNIST(path, train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST(path, train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'smallNORB': num_class = 5 train_loader = torch.utils.data.DataLoader( smallNORB(path, train=True, download=True, transform=transforms.Compose([ transforms.Resize(48), transforms.RandomCrop(32), transforms.ColorJitter(brightness=32./255, contrast=0.5), transforms.ToTensor() ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( smallNORB(path, train=False, transform=transforms.Compose([ transforms.Resize(48), transforms.CenterCrop(32), transforms.ToTensor() ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == "KTH": print("Preparing KTH data...") print("train batch size: ", args.batch_size) print("test batch size: ", args.test_batch_size) train_data = prepareDataset("/Volumes/E_128/train") test_data = prepareDataset("/Volumes/E_128/test") num_class = 6 # train classes={ "handwaving": 0, "handclapping": 1, "boxing": 2, "walking": 3, "running": 4, "jogging": 5 } action_dataset_train = ActionDataset(train_data, classes, transforms=transforms.Compose([ transforms.Resize(48), transforms.RandomCrop(32), transforms.ColorJitter(brightness=32./255, contrast=0.5), transforms.ToTensor() ])) train_loader = torch.utils.data.DataLoader(action_dataset_train, batch_size=args.batch_size, shuffle=True, **kwargs) action_dataset_test = ActionDataset(test_data, classes, transforms=transforms.Compose([ transforms.Resize(48), transforms.RandomCrop(32), transforms.ToTensor() ])) test_loader = torch.utils.data.DataLoader(action_dataset_test, batch_size=args.test_batch_size, shuffle=True, **kwargs) else: raise NameError('Undefined dataset {}'.format(args.dataset)) return num_class, train_loader, test_loader
def get_setting(args): kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} path = os.path.join(args.data_folder, args.dataset) # normalize = transforms.Normalize(mean=[x/255.0 for x in [125.3, 123.0, 113.9]], # std=[x/255.0 for x in [63.0, 62.1, 66.7]]) transform_train = transforms.Compose([ transforms.Grayscale(num_output_channels=1), transforms.RandomCrop(28, padding='valid'), transforms.RandomHorizontalFlip(), transforms.ToTensor() ]) transform_test = transforms.Compose( [transforms.Grayscale(num_output_channels=1), transforms.ToTensor()]) if args.dataset == 'mnist': num_class = 10 train_loader = torch.utils.data.DataLoader(datasets.MNIST( path, train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST(path, train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'smallNORB': num_class = 5 train_loader = torch.utils.data.DataLoader(smallNORB( path, train=True, download=True, transform=transforms.Compose([ transforms.Resize(48), transforms.RandomCrop(32), transforms.ColorJitter(brightness=32. / 255, contrast=0.5), transforms.ToTensor() ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( smallNORB(path, train=False, transform=transforms.Compose([ transforms.Resize(48), transforms.CenterCrop(32), transforms.ToTensor() ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'cifar10': num_class = 10 train_loader = torch.utils.data.DataLoader(datasets.CIFAR10( path, train=True, download=True, transform=transforms.Compose([ transforms.Grayscale(num_output_channels=1), transforms.RandomCrop(32), transforms.ToTensor() ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.CIFAR10(path, train=False, transform=transforms.Compose([ transforms.Grayscale(num_output_channels=1), transforms.ToTensor() ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) else: raise NameError('Undefined dataset {}'.format(args.dataset)) return num_class, train_loader, test_loader