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from typing import List, Union
import torch
from torch.utils.data.dataloader import default_collate
from torch_geometric.data import Batch, Data, Dataset
def collate_fn(data_list: List[Data]) -> Batch:
batch = Batch()
for key in data_list[0].keys():
batch[key] = default_collate([data[key] for data in data_list])
return batch
class DenseDataLoader(torch.utils.data.DataLoader):
r"""A data loader which batches data objects from a
:class:`torch_geometric.data.dataset` to a
:class:`torch_geometric.data.Batch` object by stacking all attributes in a
new dimension.
.. note::
To make use of this data loader, all graph attributes in the dataset
need to have the same shape.
In particular, this data loader should only be used when working with
*dense* adjacency matrices.
Args:
dataset (Dataset): The dataset from which to load the data.
batch_size (int, optional): How many samples per batch to load.
(default: :obj:`1`)
shuffle (bool, optional): If set to :obj:`True`, the data will be
reshuffled at every epoch. (default: :obj:`False`)
**kwargs (optional): Additional arguments of
:class:`torch.utils.data.DataLoader`, such as :obj:`drop_last` or
:obj:`num_workers`.
"""
def __init__(self, dataset: Union[Dataset, List[Data]],
batch_size: int = 1, shuffle: bool = False, **kwargs):
# Remove for PyTorch Lightning:
kwargs.pop('collate_fn', None)
super().__init__(dataset, batch_size=batch_size, shuffle=shuffle,
collate_fn=collate_fn, **kwargs)
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