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
189 行
4.8 KiB
Python
189 行
4.8 KiB
Python
import dgl
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from dgl import DGLGraph
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import backend as F
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def tree1():
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"""Generate a tree
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0
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/ \
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1 2
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/ \
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3 4
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Edges are from leaves to root.
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"""
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g = dgl.DGLGraph()
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g.add_nodes(5)
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g.add_edge(3, 1)
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g.add_edge(4, 1)
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g.add_edge(1, 0)
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g.add_edge(2, 0)
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g.ndata['h'] = F.tensor([0, 1, 2, 3, 4])
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g.edata['h'] = F.randn((4, 10))
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return g
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def tree2():
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"""Generate a tree
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1
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/ \
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4 3
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/ \
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2 0
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Edges are from leaves to root.
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"""
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g = dgl.DGLGraph()
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g.add_nodes(5)
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g.add_edge(2, 4)
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g.add_edge(0, 4)
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g.add_edge(4, 1)
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g.add_edge(3, 1)
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g.ndata['h'] = F.tensor([0, 1, 2, 3, 4])
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g.edata['h'] = F.randn((4, 10))
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return g
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def test_batch_unbatch():
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t1 = tree1()
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t2 = tree2()
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bg = dgl.batch([t1, t2])
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assert bg.number_of_nodes() == 10
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assert bg.number_of_edges() == 8
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assert bg.batch_size == 2
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assert bg.batch_num_nodes == [5, 5]
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assert bg.batch_num_edges == [4, 4]
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tt1, tt2 = dgl.unbatch(bg)
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assert F.allclose(t1.ndata['h'], tt1.ndata['h'])
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assert F.allclose(t1.edata['h'], tt1.edata['h'])
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assert F.allclose(t2.ndata['h'], tt2.ndata['h'])
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assert F.allclose(t2.edata['h'], tt2.edata['h'])
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def test_batch_unbatch1():
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t1 = tree1()
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t2 = tree2()
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b1 = dgl.batch([t1, t2])
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b2 = dgl.batch([t2, b1])
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assert b2.number_of_nodes() == 15
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assert b2.number_of_edges() == 12
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assert b2.batch_size == 3
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assert b2.batch_num_nodes == [5, 5, 5]
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assert b2.batch_num_edges == [4, 4, 4]
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s1, s2, s3 = dgl.unbatch(b2)
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assert F.allclose(t2.ndata['h'], s1.ndata['h'])
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assert F.allclose(t2.edata['h'], s1.edata['h'])
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assert F.allclose(t1.ndata['h'], s2.ndata['h'])
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assert F.allclose(t1.edata['h'], s2.edata['h'])
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assert F.allclose(t2.ndata['h'], s3.ndata['h'])
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assert F.allclose(t2.edata['h'], s3.edata['h'])
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def test_batch_unbatch2():
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# test setting/getting features after batch
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a = dgl.DGLGraph()
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a.add_nodes(4)
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a.add_edges(0, [1, 2, 3])
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b = dgl.DGLGraph()
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b.add_nodes(3)
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b.add_edges(0, [1, 2])
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c = dgl.batch([a, b])
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c.ndata['h'] = F.ones((7, 1))
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c.edata['w'] = F.ones((5, 1))
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assert F.allclose(c.ndata['h'], F.ones((7, 1)))
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assert F.allclose(c.edata['w'], F.ones((5, 1)))
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def test_batch_send_then_recv():
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t1 = tree1()
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t2 = tree2()
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bg = dgl.batch([t1, t2])
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bg.register_message_func(lambda edges: {'m' : edges.src['h']})
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bg.register_reduce_func(lambda nodes: {'h' : F.sum(nodes.mailbox['m'], 1)})
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u = [3, 4, 2 + 5, 0 + 5]
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v = [1, 1, 4 + 5, 4 + 5]
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bg.send((u, v))
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bg.recv([1, 9]) # assuming recv takes in unique nodes
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t1, t2 = dgl.unbatch(bg)
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assert t1.ndata['h'][1] == 7
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assert t2.ndata['h'][4] == 2
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def test_batch_send_and_recv():
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t1 = tree1()
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t2 = tree2()
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bg = dgl.batch([t1, t2])
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bg.register_message_func(lambda edges: {'m' : edges.src['h']})
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bg.register_reduce_func(lambda nodes: {'h' : F.sum(nodes.mailbox['m'], 1)})
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u = [3, 4, 2 + 5, 0 + 5]
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v = [1, 1, 4 + 5, 4 + 5]
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bg.send_and_recv((u, v))
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t1, t2 = dgl.unbatch(bg)
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assert t1.ndata['h'][1] == 7
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assert t2.ndata['h'][4] == 2
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def test_batch_propagate():
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t1 = tree1()
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t2 = tree2()
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bg = dgl.batch([t1, t2])
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bg.register_message_func(lambda edges: {'m' : edges.src['h']})
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bg.register_reduce_func(lambda nodes: {'h' : F.sum(nodes.mailbox['m'], 1)})
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# get leaves.
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order = []
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# step 1
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u = [3, 4, 2 + 5, 0 + 5]
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v = [1, 1, 4 + 5, 4 + 5]
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order.append((u, v))
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# step 2
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u = [1, 2, 4 + 5, 3 + 5]
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v = [0, 0, 1 + 5, 1 + 5]
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order.append((u, v))
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bg.prop_edges(order)
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t1, t2 = dgl.unbatch(bg)
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assert t1.ndata['h'][0] == 9
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assert t2.ndata['h'][1] == 5
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def test_batched_edge_ordering():
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g1 = dgl.DGLGraph()
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g1.add_nodes(6)
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g1.add_edges([4, 4, 2, 2, 0], [5, 3, 3, 1, 1])
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e1 = F.randn((5, 10))
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g1.edata['h'] = e1
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g2 = dgl.DGLGraph()
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g2.add_nodes(6)
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g2.add_edges([0, 1 ,2 ,5, 4 ,5], [1, 2, 3, 4, 3, 0])
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e2 = F.randn((6, 10))
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g2.edata['h'] = e2
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g = dgl.batch([g1, g2])
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r1 = g.edata['h'][g.edge_id(4, 5)]
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r2 = g1.edata['h'][g1.edge_id(4, 5)]
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assert F.array_equal(r1, r2)
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def test_batch_no_edge():
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g1 = dgl.DGLGraph()
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g1.add_nodes(6)
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g1.add_edges([4, 4, 2, 2, 0], [5, 3, 3, 1, 1])
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g2 = dgl.DGLGraph()
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g2.add_nodes(6)
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g2.add_edges([0, 1, 2, 5, 4, 5], [1 ,2 ,3, 4, 3, 0])
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g3 = dgl.DGLGraph()
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g3.add_nodes(1) # no edges
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g = dgl.batch([g1, g3, g2]) # should not throw an error
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if __name__ == '__main__':
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test_batch_unbatch()
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test_batch_unbatch1()
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test_batch_unbatch2()
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test_batched_edge_ordering()
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test_batch_send_then_recv()
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test_batch_send_and_recv()
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test_batch_propagate()
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test_batch_no_edge()
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