Sequential

class dgl.nn.mxnet.utils.Sequential(prefix=None, params=None)

Bases: mxnet.gluon.nn.basic_layers.Sequential

A squential container for stacking graph neural network blocks

We support two modes: sequentially apply GNN blocks on the same graph or a list of given graphs. In the second case, the number of graphs equals the number of blocks inside this container.

Examples

Mode 1: sequentially apply GNN modules on the same graph

>>> import dgl
>>> from mxnet import nd
>>> from mxnet.gluon import nn
>>> import dgl.function as fn
>>> from dgl.nn.mxnet import Sequential
>>> class ExampleLayer(nn.Block):
>>>     def __init__(self, **kwargs):
>>>         super().__init__(**kwargs)
>>>     def forward(self, graph, n_feat, e_feat):
>>>         with graph.local_scope():
>>>             graph.ndata['h'] = n_feat
>>>             graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
>>>             n_feat += graph.ndata['h']
>>>             graph.apply_edges(fn.u_add_v('h', 'h', 'e'))
>>>             e_feat += graph.edata['e']
>>>             return n_feat, e_feat
>>>
>>> g = dgl.DGLGraph()
>>> g.add_nodes(3)
>>> g.add_edges([0, 1, 2, 0, 1, 2, 0, 1, 2], [0, 0, 0, 1, 1, 1, 2, 2, 2])
>>> net = Sequential()
>>> net.add(ExampleLayer())
>>> net.add(ExampleLayer())
>>> net.add(ExampleLayer())
>>> net.initialize()
>>> n_feat = nd.random.randn(3, 4)
>>> e_feat = nd.random.randn(9, 4)
>>> net(g, n_feat, e_feat)
(
[[ 12.412863   99.61184    21.472883  -57.625923 ]
 [ 10.08097   100.68611    20.627377  -60.13458  ]
 [ 11.7912245 101.80654    22.427956  -58.32772  ]]
<NDArray 3x4 @cpu(0)>,
[[  21.818504  198.12076    42.72387  -115.147736]
 [  23.070837  195.49811    43.42292  -116.17203 ]
 [  24.330334  197.10927    42.40048  -118.06538 ]
 [  21.907919  199.11469    42.1187   -115.35658 ]
 [  22.849625  198.79213    43.866085 -113.65381 ]
 [  20.926125  198.116      42.64334  -114.246704]
 [  23.003159  197.06662    41.796425 -117.14977 ]
 [  21.391375  198.3348     41.428078 -116.30361 ]
 [  21.291483  200.0701     40.8239   -118.07314 ]]
<NDArray 9x4 @cpu(0)>)

Mode 2: sequentially apply GNN modules on different graphs

>>> import dgl
>>> from mxnet import nd
>>> from mxnet.gluon import nn
>>> import dgl.function as fn
>>> import networkx as nx
>>> from dgl.nn.mxnet import Sequential
>>> class ExampleLayer(nn.Block):
>>>     def __init__(self, **kwargs):
>>>         super().__init__(**kwargs)
>>>     def forward(self, graph, n_feat):
>>>         with graph.local_scope():
>>>             graph.ndata['h'] = n_feat
>>>             graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
>>>             n_feat += graph.ndata['h']
>>>             return n_feat.reshape(graph.num_nodes() // 2, 2, -1).sum(1)
>>>
>>> g1 = dgl.DGLGraph(nx.erdos_renyi_graph(32, 0.05))
>>> g2 = dgl.DGLGraph(nx.erdos_renyi_graph(16, 0.2))
>>> g3 = dgl.DGLGraph(nx.erdos_renyi_graph(8, 0.8))
>>> net = Sequential()
>>> net.add(ExampleLayer())
>>> net.add(ExampleLayer())
>>> net.add(ExampleLayer())
>>> net.initialize()
>>> n_feat = nd.random.randn(32, 4)
>>> net([g1, g2, g3], n_feat)
[[-101.289566  -22.584694  -89.25348  -151.6447  ]
 [-130.74239   -49.494812 -120.250854 -199.81546 ]
 [-112.32089   -50.036713 -116.13266  -190.38638 ]
 [-119.23065   -26.78553  -111.11185  -166.08322 ]]
<NDArray 4x4 @cpu(0)>

forward(graph, *feats)

Sequentially apply modules to the input.

Parameters

• graph (DGLGraph or list of DGLGraphs) – The graph(s) to apply modules on.

• *feats – Input features. The output of \(i\)-th block should match that of the input of \((i+1)\)-th block.