wehub-resource-sync
188 lines
6.4 KiB
Python
188 lines
6.4 KiB
Python
"""Torch Module for NNConv 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 torch.nn import init
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from .... import function as fn
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from ....utils import expand_as_pair
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from ..utils import Identity
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class NNConv(nn.Module):
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r"""Graph Convolution layer from `Neural Message Passing
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for Quantum Chemistry <https://arxiv.org/pdf/1704.01212.pdf>`__
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.. math::
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h_{i}^{l+1} = h_{i}^{l} + \mathrm{aggregate}\left(\left\{
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f_\Theta (e_{ij}) \cdot h_j^{l}, j\in \mathcal{N}(i) \right\}\right)
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where :math:`e_{ij}` is the edge feature, :math:`f_\Theta` is a function
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with learnable parameters.
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Parameters
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----------
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in_feats : int
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Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
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NNConv can be applied on homogeneous graph and unidirectional
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`bipartite graph <https://docs.dgl.ai/generated/dgl.bipartite.html?highlight=bipartite>`__.
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If the layer is to be applied on a unidirectional bipartite graph, ``in_feats``
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specifies the input feature size on both the source and destination nodes. If
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a scalar is given, the source and destination node feature size would take the
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same value.
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out_feats : int
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Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`.
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edge_func : callable activation function/layer
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Maps each edge feature to a vector of shape
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``(in_feats * out_feats)`` as weight to compute
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messages.
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Also is the :math:`f_\Theta` in the formula.
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aggregator_type : str
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Aggregator type to use (``sum``, ``mean`` or ``max``).
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residual : bool, optional
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If True, use residual connection. Default: ``False``.
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bias : bool, optional
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If True, adds a learnable bias to the output. Default: ``True``.
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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 NNConv
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>>> # Case 1: Homogeneous graph
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> g = dgl.add_self_loop(g)
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>>> feat = th.ones(6, 10)
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>>> lin = th.nn.Linear(5, 20)
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>>> def edge_func(efeat):
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... return lin(efeat)
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>>> efeat = th.ones(6+6, 5)
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>>> conv = NNConv(10, 2, edge_func, 'mean')
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>>> res = conv(g, feat, efeat)
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>>> res
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tensor([[-1.5243, -0.2719],
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[-1.5243, -0.2719],
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[-1.5243, -0.2719],
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[-1.5243, -0.2719],
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[-1.5243, -0.2719],
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[-1.5243, -0.2719]], grad_fn=<AddBackward0>)
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>>> # Case 2: Unidirectional bipartite graph
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>>> u = [0, 1, 0, 0, 1]
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>>> v = [0, 1, 2, 3, 2]
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>>> g = dgl.heterograph({('_N', '_E', '_N'):(u, v)})
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>>> u_feat = th.tensor(np.random.rand(2, 10).astype(np.float32))
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>>> v_feat = th.tensor(np.random.rand(4, 10).astype(np.float32))
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>>> conv = NNConv(10, 2, edge_func, 'mean')
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>>> efeat = th.ones(5, 5)
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>>> res = conv(g, (u_feat, v_feat), efeat)
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>>> res
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tensor([[-0.6568, 0.5042],
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[ 0.9089, -0.5352],
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[ 0.1261, -0.0155],
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[-0.6568, 0.5042]], grad_fn=<AddBackward0>)
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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edge_func,
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aggregator_type="mean",
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residual=False,
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bias=True,
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):
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super(NNConv, self).__init__()
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self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats)
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self._out_feats = out_feats
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self.edge_func = edge_func
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if aggregator_type == "sum":
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self.reducer = fn.sum
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elif aggregator_type == "mean":
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self.reducer = fn.mean
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elif aggregator_type == "max":
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self.reducer = fn.max
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else:
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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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self._aggre_type = aggregator_type
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if residual:
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if self._in_dst_feats != out_feats:
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self.res_fc = nn.Linear(
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self._in_dst_feats, out_feats, bias=False
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)
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else:
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self.res_fc = Identity()
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else:
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self.register_buffer("res_fc", None)
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if bias:
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self.bias = nn.Parameter(th.Tensor(out_feats))
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else:
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self.register_buffer("bias", None)
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self.reset_parameters()
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def reset_parameters(self):
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r"""
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Description
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-----------
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Reinitialize learnable parameters.
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Note
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----
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The model parameters are initialized using Glorot uniform initialization
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and the bias is initialized to be zero.
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"""
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gain = init.calculate_gain("relu")
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if self.bias is not None:
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nn.init.zeros_(self.bias)
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if isinstance(self.res_fc, nn.Linear):
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nn.init.xavier_normal_(self.res_fc.weight, gain=gain)
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def forward(self, graph, feat, efeat):
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r"""Compute MPNN Graph Convolution 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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The input feature of shape :math:`(N, D_{in})` where :math:`N`
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is the number of nodes of the graph and :math:`D_{in}` is the
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input feature size.
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efeat : torch.Tensor
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The edge feature of shape :math:`(E, *)`, which should fit the input
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shape requirement of ``edge_func``. :math:`E` is the number of edges
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of the graph.
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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 :math:`D_{out}`
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is the output feature size.
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"""
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with graph.local_scope():
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feat_src, feat_dst = expand_as_pair(feat, graph)
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# (n, d_in, 1)
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graph.srcdata["h"] = feat_src.unsqueeze(-1)
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# (n, d_in, d_out)
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graph.edata["w"] = self.edge_func(efeat).view(
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-1, self._in_src_feats, self._out_feats
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)
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# (n, d_in, d_out)
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graph.update_all(
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fn.u_mul_e("h", "w", "m"), self.reducer("m", "neigh")
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)
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rst = graph.dstdata["neigh"].sum(dim=1) # (n, d_out)
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# residual connection
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if self.res_fc is not None:
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rst = rst + self.res_fc(feat_dst)
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# bias
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if self.bias is not None:
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rst = rst + self.bias
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return rst
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