wehub-resource-sync
202 lines
7.6 KiB
Python
202 lines
7.6 KiB
Python
"""Torch Module for EdgeConv Layer"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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from torch import nn
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from .... import function as fn
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from ....base import DGLError
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from ....utils import expand_as_pair
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class EdgeConv(nn.Module):
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r"""EdgeConv layer from `Dynamic Graph CNN for Learning on Point Clouds
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<https://arxiv.org/pdf/1801.07829>`__
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It can be described as follows:
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.. math::
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h_i^{(l+1)} = \max_{j \in \mathcal{N}(i)} (
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\Theta \cdot (h_j^{(l)} - h_i^{(l)}) + \Phi \cdot h_i^{(l)})
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where :math:`\mathcal{N}(i)` is the neighbor of :math:`i`.
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:math:`\Theta` and :math:`\Phi` are linear layers.
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.. note::
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The original formulation includes a ReLU inside the maximum operator.
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This is equivalent to first applying a maximum operator then applying
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the ReLU.
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Parameters
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----------
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in_feat : int
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Input feature size; i.e, the number of dimensions of :math:`h_j^{(l)}`.
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out_feat : int
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Output feature size; i.e., the number of dimensions of :math:`h_i^{(l+1)}`.
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batch_norm : bool
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Whether to include batch normalization on messages. Default: ``False``.
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allow_zero_in_degree : bool, optional
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If there are 0-in-degree nodes in the graph, output for those nodes will be invalid
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since no message will be passed to those nodes. This is harmful for some applications
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causing silent performance regression. This module will raise a DGLError if it detects
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0-in-degree nodes in input graph. By setting ``True``, it will suppress the check
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and let the users handle it by themselves. Default: ``False``.
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Note
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----
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Zero in-degree nodes will lead to invalid output value. This is because no message
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will be passed to those nodes, the aggregation function will be appied on empty input.
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A common practice to avoid this is to add a self-loop for each node in the graph if
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it is homogeneous, which can be achieved by:
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>>> g = ... # a DGLGraph
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>>> g = dgl.add_self_loop(g)
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Calling ``add_self_loop`` will not work for some graphs, for example, heterogeneous graph
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since the edge type can not be decided for self_loop edges. Set ``allow_zero_in_degree``
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to ``True`` for those cases to unblock the code and handle zero-in-degree nodes manually.
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A common practise to handle this is to filter out the nodes with zero-in-degree when use
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after conv.
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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 EdgeConv
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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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>>> conv = EdgeConv(10, 2)
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>>> res = conv(g, feat)
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>>> res
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tensor([[-0.2347, 0.5849],
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[-0.2347, 0.5849],
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[-0.2347, 0.5849],
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[-0.2347, 0.5849],
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[-0.2347, 0.5849],
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[-0.2347, 0.5849]], grad_fn=<CopyReduceBackward>)
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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_fea = th.rand(2, 5)
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>>> v_fea = th.rand(4, 5)
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>>> conv = EdgeConv(5, 2, 3)
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>>> res = conv(g, (u_fea, v_fea))
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>>> res
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tensor([[ 1.6375, 0.2085],
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[-1.1925, -1.2852],
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[ 0.2101, 1.3466],
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[ 0.2342, -0.9868]], grad_fn=<CopyReduceBackward>)
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"""
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def __init__(
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self, in_feat, out_feat, batch_norm=False, allow_zero_in_degree=False
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):
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super(EdgeConv, self).__init__()
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self.batch_norm = batch_norm
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self._allow_zero_in_degree = allow_zero_in_degree
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self.theta = nn.Linear(in_feat, out_feat)
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self.phi = nn.Linear(in_feat, out_feat)
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if batch_norm:
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self.bn = nn.BatchNorm1d(out_feat)
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def set_allow_zero_in_degree(self, set_value):
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r"""
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Description
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-----------
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Set allow_zero_in_degree flag.
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Parameters
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----------
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set_value : bool
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The value to be set to the flag.
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"""
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self._allow_zero_in_degree = set_value
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def forward(self, g, feat):
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"""
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Description
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-----------
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Forward computation
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Parameters
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----------
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g : DGLGraph
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The graph.
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feat : Tensor or pair of tensors
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:math:`(N, D)` where :math:`N` is the number of nodes and
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:math:`D` is the number of feature dimensions.
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If a pair of tensors is given, the graph must be a uni-bipartite graph
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with only one edge type, and the two tensors must have the same
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dimensionality on all except the first axis.
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Returns
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-------
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torch.Tensor
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New node features.
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Raises
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------
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DGLError
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If there are 0-in-degree nodes in the input graph, it will raise DGLError
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since no message will be passed to those nodes. This will cause invalid output.
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The error can be ignored by setting ``allow_zero_in_degree`` parameter to ``True``.
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"""
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with g.local_scope():
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if not self._allow_zero_in_degree:
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if (g.in_degrees() == 0).any():
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raise DGLError(
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"There are 0-in-degree nodes in the graph, "
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"output for those nodes will be invalid. "
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"This is harmful for some applications, "
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"causing silent performance regression. "
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"Adding self-loop on the input graph by "
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"calling `g = dgl.add_self_loop(g)` will resolve "
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"the issue. Setting ``allow_zero_in_degree`` "
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"to be `True` when constructing this module will "
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"suppress the check and let the code run."
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)
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h_src, h_dst = expand_as_pair(feat, g)
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g.srcdata["x"] = h_src
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g.dstdata["x"] = h_dst
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g.apply_edges(fn.v_sub_u("x", "x", "theta"))
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g.edata["theta"] = self.theta(g.edata["theta"])
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g.dstdata["phi"] = self.phi(g.dstdata["x"])
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if not self.batch_norm:
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g.update_all(fn.e_add_v("theta", "phi", "e"), fn.max("e", "x"))
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else:
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g.apply_edges(fn.e_add_v("theta", "phi", "e"))
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# Although the official implementation includes a per-edge
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# batch norm within EdgeConv, I choose to replace it with a
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# global batch norm for a number of reasons:
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#
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# (1) When the point clouds within each batch do not have the
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# same number of points, batch norm would not work.
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#
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# (2) Even if the point clouds always have the same number of
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# points, the points may as well be shuffled even with the
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# same (type of) object (and the official implementation
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# *does* shuffle the points of the same example for each
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# epoch).
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#
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# For example, the first point of a point cloud of an
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# airplane does not always necessarily reside at its nose.
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#
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# In this case, the learned statistics of each position
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# by batch norm is not as meaningful as those learned from
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# images.
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g.edata["e"] = self.bn(g.edata["e"])
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g.update_all(fn.copy_e("e", "e"), fn.max("e", "x"))
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return g.dstdata["x"]
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