pytorch_geometric：Planetoid数据集合

torch.utils.data.Dataset

Dataset是数据集的抽象类，需要实现__getitem__ 和__len__：

class Dataset(object):
    """An abstract class representing a Dataset.

    All other datasets should subclass it. All subclasses should override
    ``__len__``, that provides the size of the dataset, and ``__getitem__``,
    supporting integer indexing in range from 0 to len(self) exclusive.
    """

    def __getitem__(self, index):
        raise NotImplementedError

    def __len__(self):
        raise NotImplementedError

    def __add__(self, other):
        return ConcatDataset([self, other])

其中的子类TensorDataset考虑每个样本都将通过索引第一个维度上的张量来检索：

class TensorDataset(Dataset):
    """Dataset wrapping tensors.

    Each sample will be retrieved by indexing tensors along the first dimension.

    Arguments:
        *tensors (Tensor): tensors that have the same size of the first dimension.
    """

    def __init__(self, *tensors):
        assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)
        self.tensors = tensors

    def __getitem__(self, index):
        return tuple(tensor[index] for tensor in self.tensors)

    def __len__(self):
        return self.tensors[0].size(0)

操作符__add__的返回类ConcatDataset，用途：对组装不同的现有数据集很有用：

class ConcatDataset(Dataset):
    """
    Dataset to concatenate multiple datasets.
    Purpose: useful to assemble different existing datasets, possibly
    large-scale datasets as the concatenation operation is done in an
    on-the-fly manner.

    Arguments:
        datasets (sequence): List of datasets to be concatenated
    """

    @staticmethod
    def cumsum(sequence):
        r, s = [], 0
        for e in sequence:
            l = len(e)
            r.append(l + s)
            s += l
        return r

    def __init__(self, datasets):
        super(ConcatDataset, self).__init__()
        assert len(datasets) > 0, 'datasets should not be an empty iterable'
        self.datasets = list(datasets)
        self.cumulative_sizes = self.cumsum(self.datasets)

    def __len__(self):
        return self.cumulative_sizes[-1]

    def __getitem__(self, idx):
    #相当于二维数组操作拿到索引idx处的数据
    	#  bisect.bisect_right（list,idx）返回list中对应idx的合理位置
    	# bisect.bisect_right([2,4,6,8,10],3) ：1
        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
        if dataset_idx == 0:
            sample_idx = idx
        else:
            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
        return self.datasets[dataset_idx][sample_idx]

    @property
    def cummulative_sizes(self):
        warnings.warn("cummulative_sizes attribute is renamed to "
                      "cumulative_sizes", DeprecationWarning, stacklevel=2)
        return self.cumulative_sizes

子类Subset，初始化时固定子数据集大小，即是大数据集dataset的固定一部分；用途是批量化：

class Subset(Dataset):
    """
    Subset of a dataset at specified indices.

    Arguments:
        dataset (Dataset): The whole Dataset
        indices (sequence): Indices in the whole set selected for subset
    """
    def __init__(self, dataset, indices):
        self.dataset = dataset
        self.indices = indices

    def __getitem__(self, idx):
        return self.dataset[self.indices[idx]]

    def __len__(self):
        return len(self.indices)

还有一个定义的打乱数据集的函数，返回批量化大小的子数据集，其中lengths参数给出划分子数据的具体大小：

def random_split(dataset, lengths):
    """
    Randomly split a dataset into non-overlapping new datasets of given lengths.

    Arguments:
        dataset (Dataset): Dataset to be split
        lengths (sequence): lengths of splits to be produced
    """
    if sum(lengths) != len(dataset):
        raise ValueError("Sum of input lengths does not equal the length of the input dataset!")

    indices = randperm(sum(lengths))
    # _accumulate([1,2,3,4,5]) --> 1 3 6 10 15
    # 递增的取完整个indices
    return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]

---

torch_geometric\data\Dataset

class Dataset(torch.utils.data.Dataset):

torch_geometric\data\data

class Data(object):
    r"""A plain old python object modeling a single graph with various
    (optional) attributes:

torch_geometric\data\In_memory_dataset

class InMemoryDataset(Dataset):