dmlc--dgl
Minjie Wang
56 行
1.6 KiB
Python
56 行
1.6 KiB
Python
import torch as th
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import dgl
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import dgl.function as fn
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import utils as U
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def generate_graph():
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g = dgl.DGLGraph()
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g.add_nodes(10) # 10 nodes.
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h = th.arange(1, 11, dtype=th.float)
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g.ndata['h'] = h
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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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h = th.tensor([1., 2., 1., 3., 1., 4., 1., 5., 1., 6.,\
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1., 7., 1., 8., 1., 9., 10.])
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g.edata['h'] = h
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return g
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def reducer_both(nodes):
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return {'h' : th.sum(nodes.mailbox['m'], 1)}
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def test_copy_src():
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# copy_src with both fields
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g = generate_graph()
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g.register_message_func(fn.copy_src(src='h', out='m'))
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g.register_reduce_func(reducer_both)
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g.update_all()
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assert U.allclose(g.ndata['h'],
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th.tensor([10., 1., 1., 1., 1., 1., 1., 1., 1., 44.]))
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def test_copy_edge():
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# copy_edge with both fields
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g = generate_graph()
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g.register_message_func(fn.copy_edge(edge='h', out='m'))
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g.register_reduce_func(reducer_both)
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g.update_all()
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assert U.allclose(g.ndata['h'],
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th.tensor([10., 1., 1., 1., 1., 1., 1., 1., 1., 44.]))
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def test_src_mul_edge():
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# src_mul_edge with all fields
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g = generate_graph()
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g.register_message_func(fn.src_mul_edge(src='h', edge='h', out='m'))
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g.register_reduce_func(reducer_both)
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g.update_all()
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assert U.allclose(g.ndata['h'],
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th.tensor([100., 1., 1., 1., 1., 1., 1., 1., 1., 284.]))
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if __name__ == '__main__':
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test_copy_src()
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test_copy_edge()
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test_src_mul_edge()
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