dmlc--dgl
Minjie Wang
99 行
2.5 KiB
Python
99 行
2.5 KiB
Python
import torch as th
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from torch.autograd import Variable
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import numpy as np
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from dgl.graph import DGLGraph
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import utils as U
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D = 5
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def generate_graph(grad=False):
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g = DGLGraph()
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g.add_nodes(10)
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# create a graph where 0 is the source and 9 is the sink
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for i in range(1, 9):
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g.add_edge(0, i)
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g.add_edge(i, 9)
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# add a back flow from 9 to 0
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g.add_edge(9, 0)
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ncol = Variable(th.randn(10, D), requires_grad=grad)
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ecol = Variable(th.randn(17, D), requires_grad=grad)
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g.ndata['h'] = ncol
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g.edata['l'] = ecol
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return g
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def test_basics():
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g = generate_graph()
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h = g.ndata['h']
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l = g.edata['l']
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nid = [0, 2, 3, 6, 7, 9]
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sg = g.subgraph(nid)
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eid = {2, 3, 4, 5, 10, 11, 12, 13, 16}
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assert set(sg.parent_eid.numpy()) == eid
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eid = sg.parent_eid
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# the subgraph is empty initially
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assert len(sg.ndata) == 0
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assert len(sg.edata) == 0
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# the data is copied after explict copy from
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sg.copy_from_parent()
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assert len(sg.ndata) == 1
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assert len(sg.edata) == 1
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sh = sg.ndata['h']
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assert U.allclose(h[nid], sh)
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'''
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s, d, eid
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0, 1, 0
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1, 9, 1
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0, 2, 2 1
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2, 9, 3 1
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0, 3, 4 1
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3, 9, 5 1
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0, 4, 6
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4, 9, 7
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0, 5, 8
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5, 9, 9 3
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0, 6, 10 1
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6, 9, 11 1 3
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0, 7, 12 1
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7, 9, 13 1 3
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0, 8, 14
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8, 9, 15 3
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9, 0, 16 1
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'''
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assert U.allclose(l[eid], sg.edata['l'])
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# update the node/edge features on the subgraph should NOT
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# reflect to the parent graph.
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sg.ndata['h'] = th.zeros((6, D))
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assert U.allclose(h, g.ndata['h'])
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def test_merge():
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# FIXME: current impl cannot handle this case!!!
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# comment out for now to test CI
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return
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"""
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g = generate_graph()
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g.set_n_repr({'h' : th.zeros((10, D))})
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g.set_e_repr({'l' : th.zeros((17, D))})
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# subgraphs
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sg1 = g.subgraph([0, 2, 3, 6, 7, 9])
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sg1.set_n_repr({'h' : th.ones((6, D))})
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sg1.set_e_repr({'l' : th.ones((9, D))})
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sg2 = g.subgraph([0, 2, 3, 4])
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sg2.set_n_repr({'h' : th.ones((4, D)) * 2})
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sg3 = g.subgraph([5, 6, 7, 8, 9])
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sg3.set_e_repr({'l' : th.ones((4, D)) * 3})
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g.merge([sg1, sg2, sg3])
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h = g.ndata['h'][:,0]
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l = g.edata['l'][:,0]
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assert U.allclose(h, th.tensor([3., 0., 3., 3., 2., 0., 1., 1., 0., 1.]))
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assert U.allclose(l,
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th.tensor([0., 0., 1., 1., 1., 1., 0., 0., 0., 3., 1., 4., 1., 4., 0., 3., 1.]))
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"""
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if __name__ == '__main__':
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test_basics()
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#test_merge()
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