Esempio n. 1
0
def create_random_bipartite():
    g = dgl.rand_bipartite('user', 'buys', 'game', 500, 1000, 1000)
    g.nodes['user'].data['feat'] = F.ones((g.num_nodes('user'), 10), F.float32,
                                          F.cpu())
    g.nodes['game'].data['feat'] = F.ones((g.num_nodes('game'), 10), F.float32,
                                          F.cpu())
    return g
Esempio n. 2
0
    'sum': lambda nodes: {
        'v': F.sum(nodes.mailbox['m'], 1)
    },
    'min': lambda nodes: {
        'v': F.min(nodes.mailbox['m'], 1)
    },
    'max': lambda nodes: {
        'v': F.max(nodes.mailbox['m'], 1)
    }
}

graphs = [
    #    dgl.rand_graph(30, 0),
    dgl.rand_graph(100, 30),
    dgl.rand_graph(100, 3000),
    dgl.rand_bipartite(80, 160, 3000)
]

spmm_shapes = [((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)),
               ((5, 3, 1, 7), (1, 3, 7, 1)), ((1, 3, 1), (4, 1, 3)),
               ((3, 3), (1, 3)), ((1, ), (3, )), ((3, ), (1, )),
               ((1, ), (1, ))]

sddmm_shapes = [((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)),
                ((5, 3, 1, 7), (1, 3, 7, 7)), ((1, 3, 3), (4, 1, 3)),
                ((3, 3), (1, 3)), ((3, ), (3, )), ((1, ), (1, ))]


@pytest.mark.parametrize('g', graphs)
@pytest.mark.parametrize('shp', spmm_shapes)
@pytest.mark.parametrize('msg',
Esempio n. 3
0
udf_reduce = {
    'sum': lambda nodes: {
        'v': F.sum(nodes.mailbox['m'], 1)
    },
    'min': lambda nodes: {
        'v': F.min(nodes.mailbox['m'], 1)
    },
    'max': lambda nodes: {
        'v': F.max(nodes.mailbox['m'], 1)
    }
}

graphs = [
    #    dgl.rand_graph(30, 0),
    dgl.rand_graph(30, 100),
    dgl.rand_bipartite(30, 40, 300)
]

spmm_shapes = [((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)), ((3, 3), (1, 3)),
               ((1, ), (3, )), ((3, ), (1, )), ((1, ), (1, ))]

sddmm_shapes = [((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)),
                ((5, 3, 1, 7), (1, 3, 7, 7)), ((1, 3, 3), (4, 1, 3)),
                ((3, ), (3, )), ((1, ), (1, ))]

edge_softmax_shapes = [(1, ), (1, 3), (3, 4, 5)]


@pytest.mark.parametrize('g', graphs)
@pytest.mark.parametrize('shp', spmm_shapes)
@pytest.mark.parametrize('msg',
Esempio n. 4
0
udf_reduce = {
    'sum': lambda nodes: {
        'v': F.sum(nodes.mailbox['m'], 1)
    },
    'min': lambda nodes: {
        'v': F.min(nodes.mailbox['m'], 1)
    },
    'max': lambda nodes: {
        'v': F.max(nodes.mailbox['m'], 1)
    }
}

graphs = [
    #    dgl.rand_graph(30, 0),
    dgl.rand_graph(30, 100),
    dgl.rand_bipartite('_U', '_E', '_V', 30, 40, 300)
]

spmm_shapes = [((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)), ((3, 3), (1, 3)),
               ((1, ), (3, )), ((3, ), (1, )), ((1, ), (1, )), ((), ())]

sddmm_shapes = [((1, 2, 1, 3, 1), (4, 1, 3, 1, 1)),
                ((5, 3, 1, 7), (1, 3, 7, 7)), ((1, 3, 3), (4, 1, 3)),
                ((3, ), (3, )), ((1, ), (1, ))]

edge_softmax_shapes = [(1, ), (1, 3), (3, 4, 5)]


@pytest.mark.parametrize('g', graphs)
@pytest.mark.parametrize('shp', spmm_shapes)
@pytest.mark.parametrize('msg',