dmlc--dgl
Mufei Li
be444e52d9
* Update graph * Fix for dgl.graph * from_scipy * Replace canonical_etypes with relations * from_networkx * Update for hetero_from_relations * Roll back the change of canonical_etypes to relations * heterograph * bipartite * Update doc * Fix lint * Fix lint * Fix test cases * Fix * Fix * Fix * Fix * Fix * Fix * Update * Fix test * Fix * Update * Use DGLError * Update * Update * Update * Update * Fix * Fix * Fix * Fix * Fix * Fix * Fix * Fix * Update * Fix * Update * Fix * Fix * Fix * Update * Fix * Update * Fix * Update * Update * Update * Update * Update * Update * Update * Fix * Fix * Update * Update * Update * Update * Update * Update * rewrite sanity checks * delete unnecessary checks * Update * Update * Update * Update * Update * Update * Update * Update * Fix * Update * Update * Update * Fix * Fix * Fix * Update * Fix * Update * Fix * Fix * Update * Fix * Update * Fix Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com> Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com> Co-authored-by: Quan Gan <coin2028@hotmail.com>
219 行
6.5 KiB
Python
219 行
6.5 KiB
Python
import dgl
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import backend as F
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import unittest
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from test_utils import parametrize_dtype
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def tree1(idtype):
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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.graph(([], [])).astype(idtype).to(F.ctx())
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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(idtype):
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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.graph(([], [])).astype(idtype).to(F.ctx())
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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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@parametrize_dtype
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def test_batch_unbatch(idtype):
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t1 = tree1(idtype)
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t2 = tree2(idtype)
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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 F.allclose(bg.batch_num_nodes(), F.tensor([5, 5]))
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assert F.allclose(bg.batch_num_edges(), F.tensor([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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@parametrize_dtype
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def test_batch_unbatch1(idtype):
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t1 = tree1(idtype)
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t2 = tree2(idtype)
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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 F.allclose(b2.batch_num_nodes(), F.tensor([5, 5, 5]))
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assert F.allclose(b2.batch_num_edges(), F.tensor([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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@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support inplace update")
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@parametrize_dtype
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def test_batch_unbatch_frame(idtype):
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"""Test module of node/edge frames of batched/unbatched DGLGraphs.
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Also address the bug mentioned in https://github.com/dmlc/dgl/issues/1475.
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"""
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t1 = tree1(idtype)
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t2 = tree2(idtype)
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N1 = t1.number_of_nodes()
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E1 = t1.number_of_edges()
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N2 = t2.number_of_nodes()
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E2 = t2.number_of_edges()
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D = 10
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t1.ndata['h'] = F.randn((N1, D))
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t1.edata['h'] = F.randn((E1, D))
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t2.ndata['h'] = F.randn((N2, D))
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t2.edata['h'] = F.randn((E2, D))
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b1 = dgl.batch([t1, t2])
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b2 = dgl.batch([t2])
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b1.ndata['h'][:N1] = F.zeros((N1, D))
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b1.edata['h'][:E1] = F.zeros((E1, D))
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b2.ndata['h'][:N2] = F.zeros((N2, D))
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b2.edata['h'][:E2] = F.zeros((E2, D))
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assert not F.allclose(t1.ndata['h'], F.zeros((N1, D)))
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assert not F.allclose(t1.edata['h'], F.zeros((E1, D)))
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assert not F.allclose(t2.ndata['h'], F.zeros((N2, D)))
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assert not F.allclose(t2.edata['h'], F.zeros((E2, D)))
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g1, g2 = dgl.unbatch(b1)
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_g2, = dgl.unbatch(b2)
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assert F.allclose(g1.ndata['h'], F.zeros((N1, D)))
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assert F.allclose(g1.edata['h'], F.zeros((E1, D)))
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assert F.allclose(g2.ndata['h'], t2.ndata['h'])
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assert F.allclose(g2.edata['h'], t2.edata['h'])
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assert F.allclose(_g2.ndata['h'], F.zeros((N2, D)))
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assert F.allclose(_g2.edata['h'], F.zeros((E2, D)))
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@parametrize_dtype
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def test_batch_unbatch2(idtype):
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# test setting/getting features after batch
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a = dgl.graph(([], [])).astype(idtype).to(F.ctx())
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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.graph(([], [])).astype(idtype).to(F.ctx())
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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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@parametrize_dtype
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def test_batch_send_and_recv(idtype):
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t1 = tree1(idtype)
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t2 = tree2(idtype)
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bg = dgl.batch([t1, t2])
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_mfunc = lambda edges: {'m' : edges.src['h']}
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_rfunc = 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), _mfunc, _rfunc)
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t1, t2 = dgl.unbatch(bg)
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assert F.asnumpy(t1.ndata['h'][1]) == 7
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assert F.asnumpy(t2.ndata['h'][4]) == 2
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@parametrize_dtype
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def test_batch_propagate(idtype):
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t1 = tree1(idtype)
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t2 = tree2(idtype)
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bg = dgl.batch([t1, t2])
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_mfunc = lambda edges: {'m' : edges.src['h']}
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_rfunc = 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, _mfunc, _rfunc)
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t1, t2 = dgl.unbatch(bg)
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assert F.asnumpy(t1.ndata['h'][0]) == 9
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assert F.asnumpy(t2.ndata['h'][1]) == 5
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@parametrize_dtype
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def test_batched_edge_ordering(idtype):
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g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
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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.graph(([], [])).astype(idtype).to(F.ctx())
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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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@parametrize_dtype
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def test_batch_no_edge(idtype):
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g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
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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.graph(([], [])).astype(idtype).to(F.ctx())
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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.graph(([], [])).astype(idtype).to(F.ctx())
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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_unbatch_frame()
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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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