dmlc--dgl
Quan (Andy) Gan
e19cd62ecd
* test basics * batched graph & filter, mxnet filter fix * frame and function; bugfix * test graph adj and inc matrices * fixing start = 0 for mxnet * test index * inplace update & line graph * multi send recv * more tests * oops * more tests * removing old test files; readonly graphs for mxnet still kept * modifying test scripts * adding a placeholder for pytorch to reserve directory * torch 0.4.1 compat fixes * moving backend out of compute to avoid nose detection * tests guide * mx sparse-to-dense/sparse-to-numpy is buggy * oops * contribution guide for unit tests * printing incmat * printing dlpack * small push * typo * fixing duplicate entries that causes undefined behavior * move equal comparison to backend
73 行
2.2 KiB
Python
73 行
2.2 KiB
Python
import dgl
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import networkx as nx
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import backend as F
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import utils as U
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def mfunc(edges):
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return {'m' : edges.src['x']}
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def rfunc(nodes):
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msg = F.sum(nodes.mailbox['m'], 1)
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return {'x' : nodes.data['x'] + msg}
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def test_prop_nodes_bfs():
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g = dgl.DGLGraph(nx.path_graph(5))
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g.ndata['x'] = F.ones((5, 2))
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g.register_message_func(mfunc)
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g.register_reduce_func(rfunc)
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dgl.prop_nodes_bfs(g, 0)
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# pull nodes using bfs order will result in a cumsum[i] + data[i] + data[i+1]
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assert F.allclose(g.ndata['x'],
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F.tensor([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]]))
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def test_prop_edges_dfs():
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g = dgl.DGLGraph(nx.path_graph(5))
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g.register_message_func(mfunc)
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g.register_reduce_func(rfunc)
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g.ndata['x'] = F.ones((5, 2))
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dgl.prop_edges_dfs(g, 0)
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# snr using dfs results in a cumsum
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assert F.allclose(g.ndata['x'],
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F.tensor([[1., 1.], [2., 2.], [3., 3.], [4., 4.], [5., 5.]]))
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g.ndata['x'] = F.ones((5, 2))
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dgl.prop_edges_dfs(g, 0, has_reverse_edge=True)
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# result is cumsum[i] + cumsum[i-1]
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assert F.allclose(g.ndata['x'],
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F.tensor([[1., 1.], [3., 3.], [5., 5.], [7., 7.], [9., 9.]]))
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g.ndata['x'] = F.ones((5, 2))
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dgl.prop_edges_dfs(g, 0, has_nontree_edge=True)
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# result is cumsum[i] + cumsum[i+1]
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assert F.allclose(g.ndata['x'],
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F.tensor([[3., 3.], [5., 5.], [7., 7.], [9., 9.], [5., 5.]]))
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def test_prop_nodes_topo():
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# bi-directional chain
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g = dgl.DGLGraph(nx.path_graph(5))
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assert U.check_fail(dgl.prop_nodes_topo, g) # has loop
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# tree
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tree = dgl.DGLGraph()
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tree.add_nodes(5)
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tree.add_edge(1, 0)
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tree.add_edge(2, 0)
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tree.add_edge(3, 2)
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tree.add_edge(4, 2)
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tree.register_message_func(mfunc)
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tree.register_reduce_func(rfunc)
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# init node feature data
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tree.ndata['x'] = F.zeros((5, 2))
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# set all leaf nodes to be ones
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tree.nodes[[1, 3, 4]].data['x'] = F.ones((3, 2))
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dgl.prop_nodes_topo(tree)
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# root node get the sum
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assert F.allclose(tree.nodes[0].data['x'], F.tensor([[3., 3.]]))
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if __name__ == '__main__':
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test_prop_nodes_bfs()
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test_prop_edges_dfs()
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test_prop_nodes_topo()
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