我们从Python开源项目中，提取了以下13个代码示例，用于说明如何使用torchvision.datasets()。
def get_data_loader(dataset_name, batch_size=1, dataset_transforms=None, is_training_set=True, shuffle=True): if not dataset_transforms: dataset_transforms = [] trans = transforms.Compose([transforms.ToTensor()] + dataset_transforms) dataset = getattr(datasets, dataset_name) return DataLoader( dataset(root=DATA_DIR, train=is_training_set, transform=trans, download=True), batch_size=batch_size, shuffle=shuffle )
def create_dataset(opt, mode): 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).astype(np.float32), 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.data_root, train=mode, download=True) smode = 'train' if mode else 'test' ds = tnt.dataset.TensorDataset([ getattr(ds, smode+'_data'), getattr(ds, smode+'_labels')]) return ds.transform({0: train_transform if mode else convert})
def create_dataset(opt, mode): 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).astype(np.float32), 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.data_root, train=mode, download=True) smode = 'train' if mode else 'test' ds = tnt.dataset.TensorDataset([ getattr(ds, smode + '_data'), getattr(ds, smode + '_labels')]) return ds.transform({0: train_transform if mode else convert})
def __init__(self, *datasets): self.datasets = datasets
def __getitem__(self, i): return tuple(d[i] for d in self.datasets)
def __len__(self): return min(len(d) for d in self.datasets)
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 create_dataset(opt, mode,fold=0): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), lambda x: x / 255.0, # cvtransforms.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) train_transform = tnt.transform.compose([ cvtransforms.RandomHorizontalFlip(), cvtransforms.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), cvtransforms.RandomCrop(96), convert, ]) smode = 'train' if mode else 'test' ds = getattr(datasets, opt.dataset)('.', split=smode, download=True) if mode: if fold>-1: folds_idx = [map(int, v.split(' ')[:-1]) for v in [line.replace('\n', '') for line in open('./stl10_binary/fold_indices.txt')]][fold] ds = tnt.dataset.TensorDataset([ getattr(ds, 'data').transpose(0, 2, 3, 1)[folds_idx], getattr(ds, 'labels')[folds_idx].tolist()]) else: ds = tnt.dataset.TensorDataset([ getattr(ds, 'data').transpose(0, 2, 3, 1), getattr(ds, 'labels').tolist()]) else: ds = tnt.dataset.TensorDataset([ getattr(ds, 'data').transpose(0, 2, 3, 1), getattr(ds, 'labels').tolist()]) return ds.transform({0: train_transform if mode else convert})
def get_dataset(config): """Return dataset class""" torchvision_datasets = [ 'LSUN', 'CocoCaptions', 'CocoDetection', 'CIFAR10', 'CIFAR100', 'FashionMNIST', 'MNIST', 'STL10', 'SVHN', 'PhotoTour', 'SEMEION'] # unaligned_datasets = [ # 'horse2zebra' # ] if config.dataset in torchvision_datasets: dataset = getattr(datasets, config.dataset)( root=config.dataset_dir, train=config.isTrain, download=True, transform=base_transform(config)) else: dataset = get_custom_dataset(config) return dataset
def _generate_images(self, nb_batches, g_fp, r_idx, opt, show_info, queue): import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.datasets as datasets import torchvision.transforms as transforms from torch.autograd import Variable #np.random.seed(42) #random.seed(42) #torch.manual_seed(42) gen = GeneratorLearnedInputSpace(opt.width, opt.height, opt.nfeature, opt.nlayer, opt.code_size, opt.norm, n_lis_layers=opt.r_iterations, upscaling=opt.g_upscaling) if show_info: print("G:", gen) gen.cuda() prefix = "last" gen.load_state_dict(torch.load(g_fp)) gen.train() print("Generating images for checkpoint G'%s'..." % (g_fp,)) #imgs_by_riter = [[] for _ in range(1+opt.r_iterations)] images_all = [] for i in range(nb_batches): code = Variable(torch.randn(opt.batch_size, opt.code_size).cuda(), volatile=True) #for r_idx in range(1+opt.r_iterations): images, _ = gen(code, n_execute_lis_layers=r_idx) images_np = (images.data.cpu().numpy() * 255).astype(np.uint8).transpose((0, 2, 3, 1)) #from scipy import misc #print(np.average(images[0]), np.min(images[0]), np.max(images[0])) #print(np.average(images_fixed[0]), np.min(images_fixed[0]), np.max(images_fixed[0])) #misc.imshow(list(images_np)[0]) #misc.imshow(list(images_fixed)[0]) #imgs_by_riter[r_idx].extend(list(images_np)) images_all.extend(images_np) result_str = pickle.dumps({ "g_fp": g_fp, "images": images_all }, protocol=-1) queue.put(result_str)
def get_iterator(mode,opt): if (opt.imagenetpath is None): raise (RuntimeError('Where is imagenet?')) if (opt.N is None): raise (RuntimeError('Crop size not provided')) if (opt.batchSize is None): raise (RuntimeError('Batch Size not provided ')) if (opt.nthread is None): raise (RuntimeError('num threads?')) 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, cvtransforms.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.imagenetpath, 'train') if (opt.max_samples > 0): ds = datasmall.ImageFolder(traindir, tnt.transform.compose([ cvtransforms.RandomSizedCrop(opt.N), cvtransforms.RandomHorizontalFlip(), convert, ]), loader=cvload,maxSamp=opt.max_samples) else: ds =torchvision.datasets.ImageFolder(traindir, tnt.transform.compose([ cvtransforms.RandomSizedCrop(opt.N), cvtransforms.RandomHorizontalFlip(), convert, ]), loader=cvload) else: if opt.N==224: crop_scale=256 else: crop_scale=256*opt.N/224 valdir = os.path.join(opt.imagenetpath, 'val') ds = torchvision.datasets.ImageFolder(valdir, tnt.transform.compose([ cvtransforms.Scale(crop_scale), cvtransforms.CenterCrop(opt.N), convert, ]), loader=cvload) return torch.utils.data.DataLoader(ds, batch_size=opt.batchSize, shuffle=mode, num_workers=opt.nthread, pin_memory=False)
def create_dataset(opt, mode): convert = tnt.transform.compose([ lambda x: x.astype(np.float32), lambda x: x / 255.0, # cvtransforms.Normalize([125.3, 123.0, 113.9], [63.0, 62.1, 66.7]), lambda x: x.transpose(2, 0, 1).astype(np.float32), torch.from_numpy, ]) train_transform = tnt.transform.compose([ cvtransforms.RandomHorizontalFlip(), cvtransforms.Pad(opt.randomcrop_pad, cv2.BORDER_REFLECT), cvtransforms.RandomCrop(32), convert, ]) ds = getattr(datasets, opt.dataset)('.', train=mode, download=True) smode = 'train' if mode else 'test' if mode: from numpy.random import RandomState prng = RandomState(opt.seed) assert(opt.sampleSize%10==0) random_permute=prng.permutation(np.arange(0,5000))[0:opt.sampleSize/10] labels = np.array(getattr(ds,'train_labels')) data = getattr(ds,'train_data') classes=np.unique(labels) inds_all=np.array([],dtype='int32') for cl in classes: inds=np.where(np.array(labels)==cl)[0][random_permute] inds_all=np.r_[inds,inds_all] ds = tnt.dataset.TensorDataset([ data[inds_all,:].transpose(0, 2, 3, 1), labels[inds_all].tolist()]) else: ds = tnt.dataset.TensorDataset([ getattr(ds, smode + '_data').transpose(0, 2, 3, 1), getattr(ds, smode + '_labels')]) return ds.transform({0: train_transform if mode else convert})
