Question

这是一个代码片段，用于从pytorch transfer learning tutorial加载图像作为数据集：

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}

dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}

这是数据集中的示例之一：

image_datasets['val'][0]:

(tensor([[[ 2.2489,  2.2489,  2.2489,  ...,  2.2489,  2.2489,  2.2489],
          [ 2.2489,  2.2489,  2.2489,  ...,  2.2489,  2.2489,  2.2489],
          [ 2.2489,  2.2489,  2.2489,  ...,  2.2489,  2.2489,  2.2489],
          ...,
          [ 2.2489,  2.2489,  2.2489,  ...,  2.2489,  2.2489,  2.2489],
          [ 2.2489,  2.2489,  2.2489,  ...,  2.2489,  2.2489,  2.2489],
          [ 2.2489,  2.2489,  2.2489,  ...,  2.2489,  2.2489,  2.2489]],

         [[ 2.4286,  2.4286,  2.4286,  ...,  2.4286,  2.4286,  2.4286],
          [ 2.4286,  2.4286,  2.4286,  ...,  2.4286,  2.4286,  2.4286],
          [ 2.4286,  2.4286,  2.4286,  ...,  2.4286,  2.4286,  2.4286],
          ...,
          [ 2.4286,  2.4286,  2.4286,  ...,  2.4286,  2.4286,  2.4286],
          [ 2.4286,  2.4286,  2.4286,  ...,  2.4286,  2.4286,  2.4286],
          [ 2.4286,  2.4286,  2.4286,  ...,  2.4286,  2.4286,  2.4286]],

         [[ 2.6400,  2.6400,  2.6400,  ...,  2.6400,  2.6400,  2.6400],
          [ 2.6400,  2.6400,  2.6400,  ...,  2.6400,  2.6400,  2.6400],
          [ 2.6400,  2.6400,  2.6400,  ...,  2.6400,  2.6400,  2.6400],
          ...,
          [ 2.6400,  2.6400,  2.6400,  ...,  2.6400,  2.6400,  2.6400],
          [ 2.6400,  2.6400,  2.6400,  ...,  2.6400,  2.6400,  2.6400],
          [ 2.6400,  2.6400,  2.6400,  ...,  2.6400,  2.6400,  2.6400]]]), 0)

是否有任何方法（最佳实践）来更改数据集中的示例数据，例如将标签0更改为标签1。以下操作无效：

image_datasets['val'][0] = (image_datasets['val'][0][0], 1)

Answer 1

是的，尽管不是（轻松地）以编程方式。标签来自torchvision.datasets.ImageFolder，并且反映了数据集的目录结构（如在硬盘上看到的）。首先，我怀疑您可能想知道目录名作为字符串。这方面的文献很少，但是数据加载器具有classes属性来存储这些属性。所以

img, lbl = image_datasets['val'][0]
directory_name = image_datasets['val'].classes[lbl]

如果您希望一致地返回这些ID而不是类ID，则可以按以下方式使用target_transform API：

image_datasets['val'].target_transform = lambda id: image_datasets['val'].classes[id]

这将使加载器从现在开始返回字符串而不是ID。如果您正在寻找更高级的内容，则可以从ImageFolder或DatasetFolder重新实现/继承，并实现自己的语义。您唯一需要提供的方法是__len__和__getitem__。

pytorch修改数据集标签

1 个答案: