chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""Torch Module for Graph Isomorphism Network layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import torch as th
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from torch import nn
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from .... import function as fn
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from ....utils import expand_as_pair
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class GINConv(nn.Module):
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r"""Graph Isomorphism Network layer from `How Powerful are Graph
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Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__
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.. math::
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h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} +
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\mathrm{aggregate}\left(\left\{h_j^{l}, j\in\mathcal{N}(i)
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\right\}\right)\right)
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If a weight tensor on each edge is provided, the weighted graph convolution is defined as:
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.. math::
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h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} +
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\mathrm{aggregate}\left(\left\{e_{ji} h_j^{l}, j\in\mathcal{N}(i)
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\right\}\right)\right)
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where :math:`e_{ji}` is the weight on the edge from node :math:`j` to node :math:`i`.
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Please make sure that `e_{ji}` is broadcastable with `h_j^{l}`.
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Parameters
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----------
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apply_func : callable activation function/layer or None
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If not None, apply this function to the updated node feature,
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the :math:`f_\Theta` in the formula, default: None.
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aggregator_type : str
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Aggregator type to use (``sum``, ``max`` or ``mean``), default: 'sum'.
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init_eps : float, optional
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Initial :math:`\epsilon` value, default: ``0``.
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learn_eps : bool, optional
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If True, :math:`\epsilon` will be a learnable parameter. Default: ``False``.
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activation : callable activation function/layer or None, optional
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If not None, applies an activation function to the updated node features.
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Default: ``None``.
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Examples
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--------
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>>> import dgl
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>>> import numpy as np
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>>> import torch as th
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>>> from dgl.nn import GINConv
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>>>
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> feat = th.ones(6, 10)
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>>> lin = th.nn.Linear(10, 10)
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>>> conv = GINConv(lin, 'max')
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>>> res = conv(g, feat)
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>>> res
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tensor([[-0.4821, 0.0207, -0.7665, 0.5721, -0.4682, -0.2134, -0.5236, 1.2855,
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0.8843, -0.8764],
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[-0.4821, 0.0207, -0.7665, 0.5721, -0.4682, -0.2134, -0.5236, 1.2855,
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0.8843, -0.8764],
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[-0.4821, 0.0207, -0.7665, 0.5721, -0.4682, -0.2134, -0.5236, 1.2855,
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0.8843, -0.8764],
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[-0.4821, 0.0207, -0.7665, 0.5721, -0.4682, -0.2134, -0.5236, 1.2855,
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0.8843, -0.8764],
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[-0.4821, 0.0207, -0.7665, 0.5721, -0.4682, -0.2134, -0.5236, 1.2855,
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0.8843, -0.8764],
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[-0.1804, 0.0758, -0.5159, 0.3569, -0.1408, -0.1395, -0.2387, 0.7773,
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0.5266, -0.4465]], grad_fn=<AddmmBackward>)
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>>> # With activation
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>>> from torch.nn.functional import relu
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>>> conv = GINConv(lin, 'max', activation=relu)
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>>> res = conv(g, feat)
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>>> res
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tensor([[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000],
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[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000],
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[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000],
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[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000],
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[5.0118, 0.0000, 0.0000, 3.9091, 1.3371, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000],
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[2.5011, 0.0000, 0.0089, 2.0541, 0.8262, 0.0000, 0.0000, 0.1371, 0.0000,
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0.0000]], grad_fn=<ReluBackward0>)
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"""
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def __init__(
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self,
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apply_func=None,
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aggregator_type="sum",
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init_eps=0,
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learn_eps=False,
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activation=None,
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):
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super(GINConv, self).__init__()
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self.apply_func = apply_func
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self._aggregator_type = aggregator_type
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self.activation = activation
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if aggregator_type not in ("sum", "max", "mean"):
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raise KeyError(
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"Aggregator type {} not recognized.".format(aggregator_type)
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)
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# to specify whether eps is trainable or not.
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if learn_eps:
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self.eps = th.nn.Parameter(th.FloatTensor([init_eps]))
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else:
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self.register_buffer("eps", th.FloatTensor([init_eps]))
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def forward(self, graph, feat, edge_weight=None):
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r"""
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Description
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-----------
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Compute Graph Isomorphism Network layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : torch.Tensor or pair of torch.Tensor
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If a torch.Tensor is given, the input feature of shape :math:`(N, D_{in})` where
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:math:`D_{in}` is size of input feature, :math:`N` is the number of nodes.
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If a pair of torch.Tensor is given, the pair must contain two tensors of shape
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:math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`.
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If ``apply_func`` is not None, :math:`D_{in}` should
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fit the input dimensionality requirement of ``apply_func``.
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edge_weight : torch.Tensor, optional
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Optional tensor on the edge. If given, the convolution will weight
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with regard to the message.
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Returns
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-------
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torch.Tensor
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The output feature of shape :math:`(N, D_{out})` where
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:math:`D_{out}` is the output dimensionality of ``apply_func``.
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If ``apply_func`` is None, :math:`D_{out}` should be the same
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as input dimensionality.
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"""
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_reducer = getattr(fn, self._aggregator_type)
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with graph.local_scope():
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aggregate_fn = fn.copy_u("h", "m")
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if edge_weight is not None:
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assert edge_weight.shape[0] == graph.num_edges()
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graph.edata["_edge_weight"] = edge_weight
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aggregate_fn = fn.u_mul_e("h", "_edge_weight", "m")
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feat_src, feat_dst = expand_as_pair(feat, graph)
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graph.srcdata["h"] = feat_src
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graph.update_all(aggregate_fn, _reducer("m", "neigh"))
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rst = (1 + self.eps) * feat_dst + graph.dstdata["neigh"]
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if self.apply_func is not None:
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rst = self.apply_func(rst)
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# activation
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if self.activation is not None:
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rst = self.activation(rst)
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return rst
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