Source code for torch_geometric.loader.dense_data_loader

from typing import List, Union

import torch
from import default_collate

from 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

[docs]class DenseDataLoader( r"""A data loader which batches data objects from a :class:`` to a :class:`` 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:``, 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)