我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用torchvision.datasets.ImageFolder()。
def load_labels(data_dir,resize=(224,224)): data_transforms = { 'train': transforms.Compose([ transforms.RandomSizedCrop(max(resize)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) } dsets = {x: datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms[x]) for x in ['train']} return (dsets['train'].classes)
def load_data(resize): data_transforms = { 'train': transforms.Compose([ transforms.RandomSizedCrop(max(resize)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ #Higher scale-up for inception transforms.Scale(int(max(resize)/224*256)), transforms.CenterCrop(max(resize)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } data_dir = 'PlantVillage' dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']} dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=batch_size, shuffle=True) for x in ['train', 'val']} dset_sizes = {x: len(dsets[x]) for x in ['train', 'val']} dset_classes = dsets['train'].classes return dset_loaders['train'], dset_loaders['val']
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 make_dataloader_torchvison_imagefolder(dir_data, data_transforms, ext_img, n_img_per_batch, n_worker): li_class = prepare_cifar10_dataset(dir_data, ext_img) li_set = ['train', 'test'] dsets = {x: datasets.ImageFolder(join(dir_data, x), data_transforms[x]) for x in li_set} dset_loaders = {x: torch.utils.data.DataLoader( dsets[x], batch_size=n_img_per_batch, shuffle=True, num_workers=n_worker) for x in li_set} trainloader, testloader = dset_loaders[li_set[0]], dset_loaders[li_set[1]] return trainloader, testloader, li_class
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 Imagenet_LMDB_generate(imagenet_dir, output_dir, make_val=False, make_train=False): # the imagenet_dir should have direction named 'train' or 'val',with 1000 folders of raw jpeg photos train_name = 'imagenet_train_lmdb' val_name = 'imagenet_val_lmdb' def target_trans(target): return target if make_val: val_lmdb=lmdb_datasets.LMDB_generator(osp.join(output_dir,val_name)) def trans_val_data(dir): tensor = transforms.Compose([ transforms.Scale(256), transforms.CenterCrop(224), transforms.ToTensor() ])(dir) tensor=(tensor.numpy()*255).astype(np.uint8) return tensor val = datasets.ImageFolder(osp.join(imagenet_dir,'val'), trans_val_data,target_trans) val_lmdb.write_classification_lmdb(val, num_per_dataset=DATASET_SIZE) if make_train: train_lmdb = lmdb_datasets.LMDB_generator(osp.join(output_dir, train_name)) def trans_train_data(dir): tensor = transforms.Compose([ transforms.Scale(256), transforms.ToTensor() ])(dir) tensor=(tensor.numpy()*255).astype(np.uint8) return tensor train = datasets.ImageFolder(osp.join(imagenet_dir, 'train'), trans_train_data, target_trans) train.imgs=np.random.permutation(train.imgs) train_lmdb.write_classification_lmdb(train, num_per_dataset=DATASET_SIZE, write_shape=True)
def create_iterator(opt, mode): if opt.dataset.startswith('CIFAR'): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), T.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2,0,1), torch.from_numpy, ]) train_transform = tnt.transform.compose([ T.RandomHorizontalFlip(), T.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), T.RandomCrop(32), convert, ]) ds = getattr(datasets, opt.dataset)(opt.dataroot, train=mode, download=True) smode = 'train' if mode else 'test' ds = tnt.dataset.TensorDataset([getattr(ds, smode + '_data'), getattr(ds, smode + '_labels')]) ds = ds.transform({0: train_transform if mode else convert}) return ds.parallel(batch_size=opt.batchSize, shuffle=mode, num_workers=opt.nthread, pin_memory=True) elif opt.dataset == 'ImageNet': def cvload(path): img = cv2.imread(path, cv2.IMREAD_COLOR) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) return img convert = tnt.transform.compose([ lambda x: x.astype(np.float32) / 255.0, T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) print("| setting up data loader...") if mode: traindir = os.path.join(opt.dataroot, 'train') ds = datasets.ImageFolder(traindir, tnt.transform.compose([ T.RandomSizedCrop(224), T.RandomHorizontalFlip(), convert, ]), loader=cvload) else: valdir = os.path.join(opt.dataroot, 'val') ds = datasets.ImageFolder(valdir, tnt.transform.compose([ T.Scale(256), T.CenterCrop(224), convert, ]), loader=cvload) return torch.utils.data.DataLoader(ds, batch_size=opt.batchSize, shuffle=mode, num_workers=opt.nthread, pin_memory=False) else: raise ValueError('dataset not understood')
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
