Sequential¶
-
class
dgl.nn.mxnet.utils.Sequential(prefix=None, params=None)[source]¶ Bases:
mxnet.gluon.nn.basic_layers.SequentialA 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)>