Source code for dgl.nn.pytorch.conv.gcn2conv

"""Torch Module for Graph Convolutional Network via Initial residual
    and Identity mapping (GCNII) layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import math

import torch as th
from torch import nn

from .... import function as fn
from ....base import DGLError
from .graphconv import EdgeWeightNorm

[docs]class GCN2Conv(nn.Module): r"""Graph Convolutional Network via Initial residual and Identity mapping (GCNII) from `Simple and Deep Graph Convolutional Networks <>`__ It is mathematically is defined as follows: .. math:: \mathbf{h}^{(l+1)} =\left( (1 - \alpha)(\mathbf{D}^{-1/2} \mathbf{\hat{A}} \mathbf{D}^{-1/2})\mathbf{h}^{(l)} + \alpha {\mathbf{h}^{(0)}} \right) \left( (1 - \beta_l) \mathbf{I} + \beta_l \mathbf{W} \right) where :math:`\mathbf{\hat{A}}` is the adjacency matrix with self-loops, :math:`\mathbf{D}_{ii} = \sum_{j=0} \mathbf{A}_{ij}` is its diagonal degree matrix, :math:`\mathbf{h}^{(0)}` is the initial node features, :math:`\mathbf{h}^{(l)}` is the feature of layer :math:`l`, :math:`\alpha` is the fraction of initial node features, and :math:`\beta_l` is the hyperparameter to tune the strength of identity mapping. It is defined by :math:`\beta_l = \log(\frac{\lambda}{l}+1)\approx\frac{\lambda}{l}`, where :math:`\lambda` is a hyperparameter. :math:`\beta` ensures that the decay of the weight matrix adaptively increases as we stack more layers. Parameters ---------- in_feats : int Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`. layer : int the index of current layer. alpha : float The fraction of the initial input features. Default: ``0.1`` lambda_ : float The hyperparameter to ensure the decay of the weight matrix adaptively increases. Default: ``1`` project_initial_features : bool Whether to share a weight matrix between initial features and smoothed features. Default: ``True`` bias : bool, optional If True, adds a learnable bias to the output. Default: ``True``. activation : callable activation function/layer or None, optional If not None, applies an activation function to the updated node features. Default: ``None``. allow_zero_in_degree : bool, optional If there are 0-in-degree nodes in the graph, output for those nodes will be invalid since no message will be passed to those nodes. This is harmful for some applications causing silent performance regression. This module will raise a DGLError if it detects 0-in-degree nodes in input graph. By setting ``True``, it will suppress the check and let the users handle it by themselves. Default: ``False``. Note ---- Zero in-degree nodes will lead to invalid output value. This is because no message will be passed to those nodes, the aggregation function will be appied on empty input. A common practice to avoid this is to add a self-loop for each node in the graph if it is homogeneous, which can be achieved by: >>> g = ... # a DGLGraph >>> g = dgl.add_self_loop(g) Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree`` to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually. A common practise to handle this is to filter out the nodes with zero-in-degree when use after conv. Examples -------- >>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import GCN2Conv >>> # Homogeneous graph >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> feat = th.ones(6, 3) >>> g = dgl.add_self_loop(g) >>> conv1 = GCN2Conv(3, layer=1, alpha=0.5, \ ... project_initial_features=True, allow_zero_in_degree=True) >>> conv2 = GCN2Conv(3, layer=2, alpha=0.5, \ ... project_initial_features=True, allow_zero_in_degree=True) >>> res = feat >>> res = conv1(g, res, feat) >>> res = conv2(g, res, feat) >>> print(res) tensor([[1.3803, 3.3191, 2.9572], [1.3803, 3.3191, 2.9572], [1.3803, 3.3191, 2.9572], [1.4770, 3.8326, 3.2451], [1.3623, 3.2102, 2.8679], [1.3803, 3.3191, 2.9572]], grad_fn=<AddBackward0>) """ def __init__( self, in_feats, layer, alpha=0.1, lambda_=1, project_initial_features=True, allow_zero_in_degree=False, bias=True, activation=None, ): super().__init__() self._in_feats = in_feats self._project_initial_features = project_initial_features self.alpha = alpha self.beta = math.log(lambda_ / layer + 1) self._bias = bias self._activation = activation self._allow_zero_in_degree = allow_zero_in_degree self.weight1 = nn.Parameter(th.Tensor(self._in_feats, self._in_feats)) if self._project_initial_features: self.register_parameter("weight2", None) else: self.weight2 = nn.Parameter( th.Tensor(self._in_feats, self._in_feats) ) if self._bias: self.bias = nn.Parameter(th.Tensor(self._in_feats)) else: self.register_parameter("bias", None) self.reset_parameters()
[docs] def reset_parameters(self): r""" Description ----------- Reinitialize learnable parameters. """ nn.init.normal_(self.weight1) if not self._project_initial_features: nn.init.normal_(self.weight2) if self._bias: nn.init.zeros_(self.bias)
def set_allow_zero_in_degree(self, set_value): r""" Description ----------- Set allow_zero_in_degree flag. Parameters ---------- set_value : bool The value to be set to the flag. """ self._allow_zero_in_degree = set_value
[docs] def forward(self, graph, feat, feat_0, edge_weight=None): r""" Description ----------- Compute graph convolution. Parameters ---------- graph : DGLGraph The graph. feat : torch.Tensor The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is the size of input feature and :math:`N` is the number of nodes. feat_0 : torch.Tensor The initial feature of shape :math:`(N, D_{in})` edge_weight: torch.Tensor, optional edge_weight to use in the message passing process. This is equivalent to using weighted adjacency matrix in the equation above, and :math:`\tilde{D}^{-1/2}\tilde{A} \tilde{D}^{-1/2}` is based on :class:`dgl.nn.pytorch.conv.graphconv.EdgeWeightNorm`. Returns ------- torch.Tensor The output feature Raises ------ DGLError If there are 0-in-degree nodes in the input graph, it will raise DGLError since no message will be passed to those nodes. This will cause invalid output. The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``. Note ---- * Input shape: :math:`(N, *, \text{in_feats})` where * means any number of additional dimensions, :math:`N` is the number of nodes. * Output shape: :math:`(N, *, \text{out_feats})` where all but the last dimension are the same shape as the input. * Weight shape: :math:`(\text{in_feats}, \text{out_feats})`. """ with graph.local_scope(): if not self._allow_zero_in_degree: if (graph.in_degrees() == 0).any(): raise DGLError( "There are 0-in-degree nodes in the graph, " "output for those nodes will be invalid. " "This is harmful for some applications, " "causing silent performance regression. " "Adding self-loop on the input graph by " "calling `g = dgl.add_self_loop(g)` will resolve " "the issue. Setting ``allow_zero_in_degree`` " "to be `True` when constructing this module will " "suppress the check and let the code run." ) # normalize to get smoothed representation if edge_weight is None: degs = graph.in_degrees().to(feat).clamp(min=1) norm = th.pow(degs, -0.5) norm = else: edge_weight = EdgeWeightNorm("both")(graph, edge_weight) if edge_weight is None: feat = feat * norm graph.ndata["h"] = feat msg_func = fn.copy_u("h", "m") if edge_weight is not None: graph.edata["_edge_weight"] = edge_weight msg_func = fn.u_mul_e("h", "_edge_weight", "m") graph.update_all(msg_func, fn.sum("m", "h")) feat = graph.ndata.pop("h") if edge_weight is None: feat = feat * norm # scale feat = feat * (1 - self.alpha) # initial residual connection to the first layer feat_0 = feat_0[: feat.size(0)] * self.alpha feat_sum = feat + feat_0 if self._project_initial_features: feat_proj_sum = feat_sum @ self.weight1 else: feat_proj_sum = feat @ self.weight1 + feat_0 @ self.weight2 rst = (1 - self.beta) * feat_sum + self.beta * feat_proj_sum if self._bias: rst = rst + self.bias if self._activation is not None: rst = self._activation(rst) return rst
def extra_repr(self): """Set the extra representation of the module, which will come into effect when printing the model. """ summary = "in={_in_feats}" summary += ", alpha={alpha}, beta={beta}" if "self._bias" in self.__dict__: summary += ", bias={bias}" if "self._activation" in self.__dict__: summary += ", activation={_activation}" return summary.format(**self.__dict__)