AttributeError:“Image”对象没有属性“new”在尝试使用Pytorchs AlexNet照明预处理时发生

我试着训练我的模特 ImageNet 使用 inception 和 Alexnet 比如预处理。我用过 Fast-ai imagenet training script 提供脚本。 Pytorch 支持 开端 就像预处理,但是 AlexNet s公司 Lighting ,我们必须自己实施:

__imagenet_pca = {
    'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]),
    'eigvec': torch.Tensor([
        [-0.5675,  0.7192,  0.4009],
        [-0.5808, -0.0045, -0.8140],
        [-0.5836, -0.6948,  0.4203],
    ])
}

# Lighting data augmentation taken from here - https://github.com/eladhoffer/convNet.pytorch/blob/master/preprocess.py
class Lighting(object):
    """Lighting noise(AlexNet - style PCA - based noise)"""

    def __init__(self, alphastd, eigval, eigvec):
        self.alphastd = alphastd
        self.eigval = eigval
        self.eigvec = eigvec

    def __call__(self, img):
        if self.alphastd == 0:
            return img

        alpha = img.new().resize_(3).normal_(0, self.alphastd)
        rgb = self.eigvec.type_as(img).clone()\
            .mul(alpha.view(1, 3).expand(3, 3))\
            .mul(self.eigval.view(1, 3).expand(3, 3))\
            .sum(1).squeeze()
        return img.add(rgb.view(3, 1, 1).expand_as(img))

最后使用如下:

train_tfms = transforms.Compose([
        transforms.RandomResizedCrop(size),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(.4,.4,.4),
        transforms.ToTensor(),
        Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']),
        normalize,
    ])
    train_dataset = datasets.ImageFolder(traindir, train_tfms)
    train_sampler = (torch.utils.data.distributed.DistributedSampler(train_dataset)
                     if args.distributed else None)
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

但是,问题是,每当我运行脚本时,我会得到:

'AttributeError:'Image'对象没有'new'属性

抱怨这句话的是:

alpha = img.new().resize_(3).normal_(0, self.alphastd)

我不知道为什么会这样。顺便说一下,我用的是Pythorch0.4。