text: # -*-  coding: utf-8 -*-
  # @Time      :2021/3/10 16:52
  # @Author    :huangzg28153
  # @File      :dataloader.py
  # @Software  :PyCharm
  import torch.utils.data
  from torch.utils.data.dataloader import default_collate
  
  # from torch_geometric.data import Data, Batch
  from torch_geometric.data import Data
  from batch import Batch
  from torch._six import container_abcs, string_classes, int_classes
  
  
  class Collater(object):
      def __init__(self, follow_batch):
          self.follow_batch = follow_batch
  
      def collate(self, batch):
          elem = batch[0]
          if isinstance(elem, Data):
              return Batch.from_data_list(batch, self.follow_batch)
          elif isinstance(elem, torch.Tensor):
              return default_collate(batch)
          elif isinstance(elem, float):
              return torch.tensor(batch, dtype=torch.float)
          elif isinstance(elem, int_classes):
              return torch.tensor(batch)
          elif isinstance(elem, string_classes):
              return batch
          elif isinstance(elem, container_abcs.Mapping):
              return {key: self.collate([d[key] for d in batch]) for key in elem}
          elif isinstance(elem, tuple) and hasattr(elem, '_fields'):
              return type(elem)(*(self.collate(s) for s in zip(*batch)))
          elif isinstance(elem, container_abcs.Sequence):
              return [self.collate(s) for s in zip(*batch)]
  
          raise TypeError('DataLoader found invalid type: {}'.format(type(elem)))
  
      def __call__(self, batch):
          return self.collate(batch)
  
  
  class DataLoader(torch.utils.data.DataLoader):
      r"""Data loader which merges data objects from a
      :class:`torch_geometric.data.dataset` to a mini-batch.
  
      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`)
          follow_batch (list or tuple, optional): Creates assignment batch
              vectors for each key in the list. (default: :obj:`[]`)
      """
  
      def __init__(self, dataset, batch_size=1, shuffle=False, follow_batch=[],
                   **kwargs):
          super(DataLoader,
                self).__init__(dataset, batch_size, shuffle,
                               collate_fn=Collater(follow_batch), **kwargs)
  
  
  class DataListLoader(torch.utils.data.DataLoader):
      r"""Data loader which merges data objects from a
      :class:`torch_geometric.data.dataset` to a python list.
  
      .. note::
  
          This data loader should be used for multi-gpu support via
          :class:`torch_geometric.nn.DataParallel`.
  
      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`)
      """
  
      def __init__(self, dataset, batch_size=1, shuffle=False, **kwargs):
          super(DataListLoader, self).__init__(
              dataset, batch_size, shuffle,
              collate_fn=lambda data_list: data_list, **kwargs)
  
  
  class DenseCollater(object):
      def collate(self, data_list):
          batch = Batch()
          for key in data_list[0].keys:
              batch[key] = default_collate([d[key] for d in data_list])
          return batch
  
      def __call__(self, batch):
          return self.collate(batch)
  
  
  class DenseDataLoader(torch.utils.data.DataLoader):
      r"""Data loader which merges data objects from a
      :class:`torch_geometric.data.dataset` to a mini-batch.
  
      .. note::
  
          To make use of this data loader, all graphs in the dataset needs to
          have the same shape for each its attributes.
          Therefore, 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`)
      """
  
      def __init__(self, dataset, batch_size=1, shuffle=False, **kwargs):
          super(DenseDataLoader, self).__init__(
              dataset, batch_size, shuffle, collate_fn=DenseCollater(), **kwargs)
  