我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用torchvision.datasets.CIFAR10。
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 CIFAR10_loader(root, image_size, normalize=True): """ Function to load torchvision dataset object based on just image size Args: root = If your dataset is downloaded and ready to use, mention the location of this folder. Else, the dataset will be downloaded to this location image_size = Size of every image normalize = Requirement to normalize the image. Default is true """ transformations = [transforms.Scale(image_size), transforms.ToTensor()] if normalize == True: transformations.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))) cifar10_data = dset.CIFAR10(root=root, download=True, transform=transforms.Compose(transformations)) return cifar10_data
def __init__(self, opt): transform_list = [] if (opt.crop_height > 0) and (opt.crop_width > 0): transform_list.append(transforms.CenterCrop(opt.crop_height, crop_width)) elif opt.crop_size > 0: transform_list.append(transforms.CenterCrop(opt.crop_size)) transform_list.append(transforms.Scale(opt.image_size)) transform_list.append(transforms.CenterCrop(opt.image_size)) transform_list.append(transforms.ToTensor()) if opt.dataset == 'cifar10': dataset1 = datasets.CIFAR10(root = opt.dataroot, download = True, transform = transforms.Compose(transform_list)) dataset2 = datasets.CIFAR10(root = opt.dataroot, train = False, transform = transforms.Compose(transform_list)) def get_data(k): if k < len(dataset1): return dataset1[k][0] else: return dataset2[k - len(dataset1)][0] else: if opt.dataset in ['imagenet', 'folder', 'lfw']: dataset = datasets.ImageFolder(root = opt.dataroot, transform = transforms.Compose(transform_list)) elif opt.dataset == 'lsun': dataset = datasets.LSUN(db_path = opt.dataroot, classes = [opt.lsun_class + '_train'], transform = transforms.Compose(transform_list)) def get_data(k): return dataset[k][0] data_index = torch.load(os.path.join(opt.dataroot, 'data_index.pt')) train_index = data_index['train'] self.opt = opt self.get_data = get_data self.train_index = data_index['train'] self.counter = 0
def get_dataloader(opt): if opt.dataset in ['imagenet', 'folder', 'lfw']: # folder dataset dataset = dset.ImageFolder(root=opt.dataroot, transform=transforms.Compose([ transforms.Scale(opt.imageScaleSize), transforms.CenterCrop(opt.imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) elif opt.dataset == 'lsun': dataset = dset.LSUN(db_path=opt.dataroot, classes=['bedroom_train'], transform=transforms.Compose([ transforms.Scale(opt.imageScaleSize), transforms.CenterCrop(opt.imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) elif opt.dataset == 'cifar10': dataset = dset.CIFAR10(root=opt.dataroot, download=True, transform=transforms.Compose([ transforms.Scale(opt.imageSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) ) assert dataset dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=int(opt.workers)) return dataloader
def get10(batch_size, data_root='/tmp/public_dataset/pytorch', train=True, val=True, **kwargs): data_root = os.path.expanduser(os.path.join(data_root, 'cifar10-data')) num_workers = kwargs.setdefault('num_workers', 1) kwargs.pop('input_size', None) print("Building CIFAR-10 data loader with {} workers".format(num_workers)) ds = [] if train: train_loader = torch.utils.data.DataLoader( datasets.CIFAR10( 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.CIFAR10( 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 infer(dataset_dir, run_dir, output_file, start, end, repeat, log2, cpu, gpu, append, models): transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(MEAN, STD) ]) testset = datasets.CIFAR10(root=dataset_dir, train=False, download=True, transform=transform_test) models = models or os.listdir(run_dir) output_path = os.path.join(run_dir, output_file) assert not os.path.exists(output_path) or append for model in models: model_dir = os.path.join(run_dir, model) paths = glob(f"{model_dir}/*/checkpoint_best_model.t7") assert len(paths) > 0 path = os.path.abspath(paths[0]) print(f'Model: {model}') print(f'Path: {path}') if cpu: print('With CPU:') engine = PyTorchEngine(path, use_cuda=False, arch=model) infer_cifar10(testset, engine, start=start, end=end, log2=log2, repeat=repeat, output=output_path) if gpu and torch.cuda.is_available(): print('With GPU:') engine = PyTorchEngine(path, use_cuda=True, arch=model) # Warmup time_batch_size(testset, 1, engine.pred, engine.use_cuda, repeat=1) infer_cifar10(testset, engine, start=start, end=end, log2=log2, repeat=repeat, output=output_path)
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
def get_loaders(args): kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} if args.dataset == 'mnist': trainLoader = torch.utils.data.DataLoader( dset.MNIST('data/mnist', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.batchSz, shuffle=True, **kwargs) testLoader = torch.utils.data.DataLoader( dset.MNIST('data/mnist', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.batchSz, shuffle=False, **kwargs) elif args.dataset == 'cifar-10': normMean = [0.49139968, 0.48215827, 0.44653124] normStd = [0.24703233, 0.24348505, 0.26158768] normTransform = transforms.Normalize(normMean, normStd) trainTransform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normTransform ]) testTransform = transforms.Compose([ transforms.ToTensor(), normTransform ]) trainLoader = DataLoader( dset.CIFAR10(root='data/cifar', train=True, download=True, transform=trainTransform), batch_size=args.batchSz, shuffle=True, **kwargs) testLoader = DataLoader( dset.CIFAR10(root='data/cifar', train=False, download=True, transform=testTransform), batch_size=args.batchSz, shuffle=False, **kwargs) else: assert(False) return trainLoader, testLoader
