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

"""Torch Module for GMM Conv"""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from torch.nn import init

from .... import function as fn
from ....base import DGLError
from ....utils import expand_as_pair
from ..utils import Identity

[docs]class GMMConv(nn.Module): r"""Gaussian Mixture Model Convolution layer from `Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs <>`__ .. math:: u_{ij} &= f(x_i, x_j), x_j \in \mathcal{N}(i) w_k(u) &= \exp\left(-\frac{1}{2}(u-\mu_k)^T \Sigma_k^{-1} (u - \mu_k)\right) h_i^{l+1} &= \mathrm{aggregate}\left(\left\{\frac{1}{K} \sum_{k}^{K} w_k(u_{ij}), \forall j\in \mathcal{N}(i)\right\}\right) where :math:`u` denotes the pseudo-coordinates between a vertex and one of its neighbor, computed using function :math:`f`, :math:`\Sigma_k^{-1}` and :math:`\mu_k` are learnable parameters representing the covariance matrix and mean vector of a Gaussian kernel. Parameters ---------- in_feats : int Number of input features; i.e., the number of dimensions of :math:`x_i`. out_feats : int Number of output features; i.e., the number of dimensions of :math:`h_i^{(l+1)}`. dim : int Dimensionality of pseudo-coordinte; i.e, the number of dimensions of :math:`u_{ij}`. n_kernels : int Number of kernels :math:`K`. aggregator_type : str Aggregator type (``sum``, ``mean``, ``max``). Default: ``sum``. residual : bool If True, use residual connection inside this layer. Default: ``False``. bias : bool If True, adds a learnable bias to the output. Default: ``True``. 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 GMMConv >>> # Case 1: Homogeneous graph >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> g = dgl.add_self_loop(g) >>> feat = th.ones(6, 10) >>> conv = GMMConv(10, 2, 3, 2, 'mean') >>> pseudo = th.ones(12, 3) >>> res = conv(g, feat, pseudo) >>> res tensor([[-0.3462, -0.2654], [-0.3462, -0.2654], [-0.3462, -0.2654], [-0.3462, -0.2654], [-0.3462, -0.2654], [-0.3462, -0.2654]], grad_fn=<AddBackward0>) >>> # Case 2: Unidirectional bipartite graph >>> u = [0, 1, 0, 0, 1] >>> v = [0, 1, 2, 3, 2] >>> g = dgl.heterograph({('_N', '_E', '_N'):(u, v)}) >>> u_fea = th.rand(2, 5) >>> v_fea = th.rand(4, 10) >>> pseudo = th.ones(5, 3) >>> conv = GMMConv((10, 5), 2, 3, 2, 'mean') >>> res = conv(g, (u_fea, v_fea), pseudo) >>> res tensor([[-0.1107, -0.1559], [-0.1646, -0.2326], [-0.1377, -0.1943], [-0.1107, -0.1559]], grad_fn=<AddBackward0>) """ def __init__( self, in_feats, out_feats, dim, n_kernels, aggregator_type="sum", residual=False, bias=True, allow_zero_in_degree=False, ): super(GMMConv, self).__init__() self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats) self._out_feats = out_feats self._dim = dim self._n_kernels = n_kernels self._allow_zero_in_degree = allow_zero_in_degree if aggregator_type == "sum": self._reducer = fn.sum elif aggregator_type == "mean": self._reducer = fn.mean elif aggregator_type == "max": self._reducer = fn.max else: raise KeyError( "Aggregator type {} not recognized.".format(aggregator_type) ) = nn.Parameter(th.Tensor(n_kernels, dim)) self.inv_sigma = nn.Parameter(th.Tensor(n_kernels, dim)) self.fc = nn.Linear( self._in_src_feats, n_kernels * out_feats, bias=False ) if residual: if self._in_dst_feats != out_feats: self.res_fc = nn.Linear( self._in_dst_feats, out_feats, bias=False ) else: self.res_fc = Identity() else: self.register_buffer("res_fc", None) if bias: self.bias = nn.Parameter(th.Tensor(out_feats)) else: self.register_buffer("bias", None) self.reset_parameters()
[docs] def reset_parameters(self): r""" Description ----------- Reinitialize learnable parameters. Note ---- The fc parameters are initialized using Glorot uniform initialization and the bias is initialized to be zero. The mu weight is initialized using normal distribution and inv_sigma is initialized with constant value 1.0. """ gain = init.calculate_gain("relu") init.xavier_normal_(self.fc.weight, gain=gain) if isinstance(self.res_fc, nn.Linear): init.xavier_normal_(self.res_fc.weight, gain=gain) init.normal_(, 0, 0.1) init.constant_(, 1) if self.bias is not None: init.zeros_(
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, pseudo): """ Description ----------- Compute Gaussian Mixture Model Convolution layer. Parameters ---------- graph : DGLGraph The graph. feat : torch.Tensor If a single tensor is given, the input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. If a pair of tensors are given, the pair must contain two tensors of shape :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`. pseudo : torch.Tensor The pseudo coordinate tensor of shape :math:`(E, D_{u})` where :math:`E` is the number of edges of the graph and :math:`D_{u}` is the dimensionality of pseudo coordinate. Returns ------- torch.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is the output feature size. 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``. """ 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." ) feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata["h"] = self.fc(feat_src).view( -1, self._n_kernels, self._out_feats ) E = graph.num_edges() # compute gaussian weight gaussian = -0.5 * ( ( pseudo.view(E, 1, self._dim) -, self._n_kernels, self._dim) ) ** 2 ) gaussian = gaussian * ( self.inv_sigma.view(1, self._n_kernels, self._dim) ** 2 ) gaussian = th.exp(gaussian.sum(dim=-1, keepdim=True)) # (E, K, 1) graph.edata["w"] = gaussian graph.update_all(fn.u_mul_e("h", "w", "m"), self._reducer("m", "h")) rst = graph.dstdata["h"].sum(1) # residual connection if self.res_fc is not None: rst = rst + self.res_fc(feat_dst) # bias if self.bias is not None: rst = rst + self.bias return rst