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
93 行
2.8 KiB
Python
93 行
2.8 KiB
Python
import networkx as nx
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import numpy as np
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import dgl
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import dgl.function as fn
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import backend as F
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D = 5
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# line graph related
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def test_line_graph():
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N = 5
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G = dgl.DGLGraph(nx.star_graph(N))
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G.edata['h'] = F.randn((2 * N, D))
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n_edges = G.number_of_edges()
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L = G.line_graph(shared=True)
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assert L.number_of_nodes() == 2 * N
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L.ndata['h'] = F.randn((2 * N, D))
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# update node features on line graph should reflect to edge features on
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# original graph.
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u = [0, 0, 2, 3]
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v = [1, 2, 0, 0]
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eid = G.edge_ids(u, v)
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L.nodes[eid].data['h'] = F.zeros((4, D))
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assert F.allclose(G.edges[u, v].data['h'], F.zeros((4, D)))
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# adding a new node feature on line graph should also reflect to a new
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# edge feature on original graph
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data = F.randn((n_edges, D))
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L.ndata['w'] = data
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assert F.allclose(G.edata['w'], data)
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def test_no_backtracking():
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N = 5
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G = dgl.DGLGraph(nx.star_graph(N))
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L = G.line_graph(backtracking=False)
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assert L.number_of_nodes() == 2 * N
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for i in range(1, N):
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e1 = G.edge_id(0, i)
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e2 = G.edge_id(i, 0)
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assert not L.has_edge_between(e1, e2)
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assert not L.has_edge_between(e2, e1)
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# reverse graph related
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def test_reverse():
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g = dgl.DGLGraph()
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g.add_nodes(5)
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# The graph need not to be completely connected.
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g.add_edges([0, 1, 2], [1, 2, 1])
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g.ndata['h'] = F.tensor([[0.], [1.], [2.], [3.], [4.]])
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g.edata['h'] = F.tensor([[5.], [6.], [7.]])
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rg = g.reverse()
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assert g.is_multigraph == rg.is_multigraph
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assert g.number_of_nodes() == rg.number_of_nodes()
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assert g.number_of_edges() == rg.number_of_edges()
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assert F.allclose(F.astype(rg.has_edges_between([1, 2, 1], [0, 1, 2]), F.float32), F.ones((3,)))
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assert g.edge_id(0, 1) == rg.edge_id(1, 0)
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assert g.edge_id(1, 2) == rg.edge_id(2, 1)
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assert g.edge_id(2, 1) == rg.edge_id(1, 2)
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def test_reverse_shared_frames():
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g = dgl.DGLGraph()
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g.add_nodes(3)
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g.add_edges([0, 1, 2], [1, 2, 1])
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g.ndata['h'] = F.tensor([[0.], [1.], [2.]])
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g.edata['h'] = F.tensor([[3.], [4.], [5.]])
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rg = g.reverse(share_ndata=True, share_edata=True)
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assert F.allclose(g.ndata['h'], rg.ndata['h'])
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assert F.allclose(g.edata['h'], rg.edata['h'])
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assert F.allclose(g.edges[[0, 2], [1, 1]].data['h'],
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rg.edges[[1, 1], [0, 2]].data['h'])
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rg.ndata['h'] = rg.ndata['h'] + 1
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assert F.allclose(rg.ndata['h'], g.ndata['h'])
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g.edata['h'] = g.edata['h'] - 1
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assert F.allclose(rg.edata['h'], g.edata['h'])
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src_msg = fn.copy_src(src='h', out='m')
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sum_reduce = fn.sum(msg='m', out='h')
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rg.update_all(src_msg, sum_reduce)
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assert F.allclose(g.ndata['h'], rg.ndata['h'])
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if __name__ == '__main__':
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test_line_graph()
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test_no_backtracking()
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test_reverse()
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test_reverse_shared_frames()
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