我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用torchvision.datasets.CIFAR100。
def dataLoader(is_train=True, cuda=True, batch_size=64, shuffle=True): if is_train: trans = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(mean=[n/255. for n in [129.3, 124.1, 112.4]], std=[n/255. for n in [68.2, 65.4, 70.4]])] trans = transforms.Compose(trans) train_set = td.CIFAR100('data', train=True, transform=trans) size = len(train_set.train_labels) train_loader = torch.utils.data.DataLoader( train_set, batch_size=batch_size, shuffle=shuffle) else: trans = [transforms.ToTensor(), transforms.Normalize(mean=[n/255. for n in [129.3, 124.1, 112.4]], std=[n/255. for n in [68.2, 65.4, 70.4]])] trans = transforms.Compose(trans) test_set = td.CIFAR100('data', train=False, transform=trans) size = len(test_set.test_labels) train_loader = torch.utils.data.DataLoader( test_set, batch_size=batch_size, shuffle=shuffle) return train_loader, size
def get_dataset(name, split='train', transform=None, target_transform=None, download=True, datasets_path=__DATASETS_DEFAULT_PATH): train = (split == 'train') root = os.path.join(datasets_path, name) if name == 'cifar10': return datasets.CIFAR10(root=root, train=train, transform=transform, target_transform=target_transform, download=download) elif name == 'cifar100': return datasets.CIFAR100(root=root, train=train, transform=transform, target_transform=target_transform, download=download) elif name == 'mnist': return datasets.MNIST(root=root, train=train, transform=transform, target_transform=target_transform, download=download) elif name == 'stl10': return datasets.STL10(root=root, split=split, transform=transform, target_transform=target_transform, download=download) elif name == 'imagenet': if train: root = os.path.join(root, 'train') else: root = os.path.join(root, 'val') return datasets.ImageFolder(root=root, transform=transform, target_transform=target_transform)
def get100(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs): data_root = os.path.expanduser(os.path.join(data_root, 'cifar100-data')) num_workers = kwargs.setdefault('num_workers', 1) kwargs.pop('input_size', None) print("Building CIFAR-100 data loader with {} workers".format(num_workers)) ds = [] if train: train_loader = torch.utils.data.DataLoader( datasets.CIFAR100( root=data_root, train=True, download=True, transform=transforms.Compose([ transforms.Pad(4), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])), batch_size=batch_size, shuffle=True, **kwargs) ds.append(train_loader) if val: test_loader = torch.utils.data.DataLoader( datasets.CIFAR100( root=data_root, train=False, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])), batch_size=batch_size, shuffle=False, **kwargs) ds.append(test_loader) ds = ds[0] if len(ds) == 1 else ds return ds
def get_test_loader(data_dir, name, batch_size, shuffle=True, num_workers=4, pin_memory=False): """ Utility function for loading and returning a multi-process test iterator over the CIFAR-10 dataset. If using CUDA, num_workers should be set to 1 and pin_memory to True. Params ------ - data_dir: path directory to the dataset. - name: string specifying which dataset to load. Can be `cifar10`, or `cifar100`. - batch_size: how many samples per batch to load. - shuffle: whether to shuffle the dataset after every epoch. - num_workers: number of subprocesses to use when loading the dataset. - pin_memory: whether to copy tensors into CUDA pinned memory. Set it to True if using GPU. Returns ------- - data_loader: test set iterator. """ normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # define transform transform = transforms.Compose([ transforms.ToTensor(), normalize ]) if name == 'cifar10': dataset = datasets.CIFAR10(root=data_dir, train=False, download=True, transform=transform) else: dataset = datasets.CIFAR100(root=data_dir, train=False, download=True, transform=transform) data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) return data_loader
def get_data(args, train_flag=True): transform = transforms.Compose([ transforms.Scale(args.image_size), transforms.CenterCrop(args.image_size), transforms.ToTensor(), transforms.Normalize( (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) if args.dataset in ['imagenet', 'folder', 'lfw']: dataset = dset.ImageFolder(root=args.dataroot, transform=transform) elif args.dataset == 'lsun': dataset = dset.LSUN(db_path=args.dataroot, classes=['bedroom_train'], transform=transform) elif args.dataset == 'cifar10': dataset = dset.CIFAR10(root=args.dataroot, download=True, train=train_flag, transform=transform) elif args.dataset == 'cifar100': dataset = dset.CIFAR100(root=args.dataroot, download=True, train=train_flag, transform=transform) elif args.dataset == 'mnist': dataset = dset.MNIST(root=args.dataroot, download=True, train=train_flag, transform=transform) elif args.dataset == 'celeba': imdir = 'train' if train_flag else 'val' dataroot = os.path.join(args.dataroot, imdir) if args.image_size != 64: raise ValueError('the image size for CelebA dataset need to be 64!') dataset = FolderWithImages(root=dataroot, input_transform=transforms.Compose([ ALICropAndScale(), transforms.ToTensor(), transforms.Normalize( (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]), target_transform=transforms.ToTensor() ) else: raise ValueError("Unknown dataset %s" % (args.dataset)) return dataset
