Source code for

import re

import torch
from import Graph, Adjacency

[docs]def batch_graphs(graphs): return Batch.from_data_list(graphs, class_type=Graph)
[docs]class Batch(Graph): r"""A plain old python object modeling a batch of graphs as one big (dicconnected) graph. With :class:`` being the base class, all its methods can also be used here. In addition, single graphs can be reconstructed via the assignment vector :obj:`batch`, which maps each node to its respective graph identifier. """ def __init__(self, batch=None, **kwargs): super(Batch, self).__init__(**kwargs) self.batch = batch self.__data_class__ = Graph self.__slices__ = None
[docs] @staticmethod def from_data_list(data_list, class_type=None): r"""Constructs a batch object from a python list holding :class:`` objects. The assignment vector :obj:`batch` is created on the fly. Additionally, creates assignment batch vectors for each key in :obj:`follow_batch`.""" # keys = [set(data.keys) for data in data_list] keys = [set(data.keys) for data in data_list] keys = list(set.union(*keys)) assert "batch" not in keys if class_type is not None: batch = class_type() else: batch = Batch() batch.__data_class__ = data_list[0].__class__ batch.__slices__ = {key: [0] for key in keys} for key in keys: batch[key] = [] # for key in follow_batch: # batch["{}_batch".format(key)] = [] cumsum = {key: 0 for key in keys} batch.batch = [] num_nodes_cum = [0] num_edges_cum = [0] for i, data in enumerate(data_list): for key in data.keys: item = data[key] if torch.is_tensor(item) and item.dtype != torch.bool: item = item + cumsum[key] if torch.is_tensor(item): size = item.size(data.cat_dim(key, data[key])) else: size = 1 batch.__slices__[key].append(size + batch.__slices__[key][-1]) cumsum[key] = cumsum[key] + data.__inc__(key, item) batch[key].append(item) # if key in follow_batch: # item = torch.full((size,), i, dtype=torch.long) # batch["{}_batch".format(key)].append(item) num_nodes = data.num_nodes if num_nodes is not None: num_nodes_cum.append(num_nodes + num_nodes_cum[-1]) num_edges_cum.append(data.num_edges + num_edges_cum[-1]) item = torch.full((num_nodes,), i, dtype=torch.long) batch.batch.append(item) if num_nodes is None: batch.batch = None for key in batch.keys: item = batch[key][0] if torch.is_tensor(item): batch[key] =[key], dim=data_list[0].cat_dim(key, item)) elif isinstance(item, int) or isinstance(item, float): batch[key] = torch.tensor(batch[key]) elif isinstance(item, Adjacency): target = Adjacency() for k in item.keys: if item[k] is None: continue if k == "row" or k == "col": _item = [x[k] + num_nodes_cum[i] for i, x in enumerate(batch[key])], dim=item.cat_dim(k, None) ) elif k == "row_ptr": _item = [x[k][:-1] + num_edges_cum[i] for i, x in enumerate(batch[key][:-1])], dim=item.cat_dim(k, None), ) _item =[_item, batch[key][-1][k] + num_edges_cum[-2]], dim=item.cat_dim(k, None)) else: _item =[x[k] for i, x in enumerate(batch[key])], dim=item.cat_dim(k, None)) target[k] = _item batch[key] = return batch.contiguous()
[docs] def cumsum(self, key, item): r"""If :obj:`True`, the attribute :obj:`key` with content :obj:`item` should be added up cumulatively before concatenated together. .. note:: This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute. """ return bool("(index|face)", key))
@property def num_graphs(self): """Returns the number of graphs in the batch.""" return self.batch[-1].item() + 1