test_nn.py

from copy import deepcopy

import backend as F

import dgl
import dgl.function as fn
import dgl.nn.tensorflow as nn
import networkx as nx
import numpy as np
import pytest
import scipy as sp
import tensorflow as tf
from pytests_utils import parametrize_idtype
from pytests_utils.graph_cases import (
    get_cases,
    random_bipartite,
    random_dglgraph,
    random_graph,
)
from tensorflow.keras import layers


def _AXWb(A, X, W, b):
    X = tf.matmul(X, W)
    Y = tf.reshape(tf.matmul(A, tf.reshape(X, (X.shape[0], -1))), X.shape)
    return Y + b


@pytest.mark.parametrize("out_dim", [1, 2])
def test_graph_conv(out_dim):
    g = dgl.DGLGraph(nx.path_graph(3)).to(F.ctx())
    ctx = F.ctx()
    adj = tf.sparse.to_dense(
        tf.sparse.reorder(g.adjacency_matrix(transpose=True, ctx=ctx))
    )

    conv = nn.GraphConv(5, out_dim, norm="none", bias=True)
    # conv = conv
    print(conv)
    # test#1: basic
    h0 = F.ones((3, 5))
    h1 = conv(g, h0)
    assert len(g.ndata) == 0
    assert len(g.edata) == 0
    assert F.allclose(h1, _AXWb(adj, h0, conv.weight, conv.bias))
    # test#2: more-dim
    h0 = F.ones((3, 5, 5))
    h1 = conv(g, h0)
    assert len(g.ndata) == 0
    assert len(g.edata) == 0
    assert F.allclose(h1, _AXWb(adj, h0, conv.weight, conv.bias))

    conv = nn.GraphConv(5, out_dim)
    # conv = conv
    # test#3: basic
    h0 = F.ones((3, 5))
    h1 = conv(g, h0)
    assert len(g.ndata) == 0
    assert len(g.edata) == 0
    # test#4: basic
    h0 = F.ones((3, 5, 5))
    h1 = conv(g, h0)
    assert len(g.ndata) == 0
    assert len(g.edata) == 0

    conv = nn.GraphConv(5, out_dim)
    # conv = conv
    # test#3: basic
    h0 = F.ones((3, 5))
    h1 = conv(g, h0)
    assert len(g.ndata) == 0
    assert len(g.edata) == 0
    # test#4: basic
    h0 = F.ones((3, 5, 5))
    h1 = conv(g, h0)
    assert len(g.ndata) == 0
    assert len(g.edata) == 0

    # test rest_parameters
    # old_weight = deepcopy(conv.weight.data)
    # conv.reset_parameters()
    # new_weight = conv.weight.data
    # assert not F.allclose(old_weight, new_weight)


@parametrize_idtype
@pytest.mark.parametrize(
    "g",
    get_cases(["homo", "block-bipartite"], exclude=["zero-degree", "dglgraph"]),
)
@pytest.mark.parametrize("norm", ["none", "both", "right", "left"])
@pytest.mark.parametrize("weight", [True, False])
@pytest.mark.parametrize("bias", [True, False])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_graph_conv2(idtype, g, norm, weight, bias, out_dim):
    g = g.astype(idtype).to(F.ctx())
    conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias)
    ext_w = F.randn((5, out_dim))
    nsrc = g.number_of_src_nodes()
    ndst = g.number_of_dst_nodes()
    h = F.randn((nsrc, 5))
    h_dst = F.randn((ndst, out_dim))
    if weight:
        h_out = conv(g, h)
    else:
        h_out = conv(g, h, weight=ext_w)
    assert h_out.shape == (ndst, out_dim)


@parametrize_idtype
@pytest.mark.parametrize(
    "g", get_cases(["bipartite"], exclude=["zero-degree", "dglgraph"])
)
@pytest.mark.parametrize("norm", ["none", "both", "right"])
@pytest.mark.parametrize("weight", [True, False])
@pytest.mark.parametrize("bias", [True, False])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim):
    g = g.astype(idtype).to(F.ctx())
    conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias)
    ext_w = F.randn((5, out_dim))
    nsrc = g.number_of_src_nodes()
    ndst = g.number_of_dst_nodes()
    h = F.randn((nsrc, 5))
    h_dst = F.randn((ndst, out_dim))
    if weight:
        h_out = conv(g, (h, h_dst))
    else:
        h_out = conv(g, (h, h_dst), weight=ext_w)
    assert h_out.shape == (ndst, out_dim)


def test_simple_pool():
    ctx = F.ctx()
    g = dgl.DGLGraph(nx.path_graph(15)).to(F.ctx())

    sum_pool = nn.SumPooling()
    avg_pool = nn.AvgPooling()
    max_pool = nn.MaxPooling()
    sort_pool = nn.SortPooling(10)  # k = 10
    print(sum_pool, avg_pool, max_pool, sort_pool)

    # test#1: basic
    h0 = F.randn((g.number_of_nodes(), 5))
    h1 = sum_pool(g, h0)
    assert F.allclose(F.squeeze(h1, 0), F.sum(h0, 0))
    h1 = avg_pool(g, h0)
    assert F.allclose(F.squeeze(h1, 0), F.mean(h0, 0))
    h1 = max_pool(g, h0)
    assert F.allclose(F.squeeze(h1, 0), F.max(h0, 0))
    h1 = sort_pool(g, h0)
    assert h1.shape[0] == 1 and h1.shape[1] == 10 * 5 and h1.ndim == 2

    # test#2: batched graph
    g_ = dgl.DGLGraph(nx.path_graph(5)).to(F.ctx())
    bg = dgl.batch([g, g_, g, g_, g])
    h0 = F.randn((bg.number_of_nodes(), 5))
    h1 = sum_pool(bg, h0)
    truth = tf.stack(
        [
            F.sum(h0[:15], 0),
            F.sum(h0[15:20], 0),
            F.sum(h0[20:35], 0),
            F.sum(h0[35:40], 0),
            F.sum(h0[40:55], 0),
        ],
        0,
    )
    assert F.allclose(h1, truth)

    h1 = avg_pool(bg, h0)
    truth = tf.stack(
        [
            F.mean(h0[:15], 0),
            F.mean(h0[15:20], 0),
            F.mean(h0[20:35], 0),
            F.mean(h0[35:40], 0),
            F.mean(h0[40:55], 0),
        ],
        0,
    )
    assert F.allclose(h1, truth)

    h1 = max_pool(bg, h0)
    truth = tf.stack(
        [
            F.max(h0[:15], 0),
            F.max(h0[15:20], 0),
            F.max(h0[20:35], 0),
            F.max(h0[35:40], 0),
            F.max(h0[40:55], 0),
        ],
        0,
    )
    assert F.allclose(h1, truth)

    h1 = sort_pool(bg, h0)
    assert h1.shape[0] == 5 and h1.shape[1] == 10 * 5 and h1.ndim == 2


def test_glob_att_pool():
    g = dgl.DGLGraph(nx.path_graph(10)).to(F.ctx())

    gap = nn.GlobalAttentionPooling(layers.Dense(1), layers.Dense(10))
    print(gap)

    # test#1: basic
    h0 = F.randn((g.number_of_nodes(), 5))
    h1 = gap(g, h0)
    assert h1.shape[0] == 1 and h1.shape[1] == 10 and h1.ndim == 2

    # test#2: batched graph
    bg = dgl.batch([g, g, g, g])
    h0 = F.randn((bg.number_of_nodes(), 5))
    h1 = gap(bg, h0)
    assert h1.shape[0] == 4 and h1.shape[1] == 10 and h1.ndim == 2


@pytest.mark.parametrize("O", [1, 2, 8])
def test_rgcn(O):
    etype = []
    g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True).to(
        F.ctx()
    )
    # 5 etypes
    R = 5
    for i in range(g.number_of_edges()):
        etype.append(i % 5)
    B = 2
    I = 10

    rgc_basis = nn.RelGraphConv(I, O, R, "basis", B)
    rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True)
    rgc_basis_low.weight = rgc_basis.weight
    rgc_basis_low.w_comp = rgc_basis.w_comp
    rgc_basis_low.loop_weight = rgc_basis.loop_weight
    h = tf.random.normal((100, I))
    r = tf.constant(etype)
    h_new = rgc_basis(g, h, r)
    h_new_low = rgc_basis_low(g, h, r)
    assert list(h_new.shape) == [100, O]
    assert list(h_new_low.shape) == [100, O]
    assert F.allclose(h_new, h_new_low)

    if O % B == 0:
        rgc_bdd = nn.RelGraphConv(I, O, R, "bdd", B)
        rgc_bdd_low = nn.RelGraphConv(I, O, R, "bdd", B, low_mem=True)
        rgc_bdd_low.weight = rgc_bdd.weight
        rgc_bdd_low.loop_weight = rgc_bdd.loop_weight
        h = tf.random.normal((100, I))
        r = tf.constant(etype)
        h_new = rgc_bdd(g, h, r)
        h_new_low = rgc_bdd_low(g, h, r)
        assert list(h_new.shape) == [100, O]
        assert list(h_new_low.shape) == [100, O]
        assert F.allclose(h_new, h_new_low)

    # with norm
    norm = tf.zeros((g.number_of_edges(), 1))

    rgc_basis = nn.RelGraphConv(I, O, R, "basis", B)
    rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True)
    rgc_basis_low.weight = rgc_basis.weight
    rgc_basis_low.w_comp = rgc_basis.w_comp
    rgc_basis_low.loop_weight = rgc_basis.loop_weight
    h = tf.random.normal((100, I))
    r = tf.constant(etype)
    h_new = rgc_basis(g, h, r, norm)
    h_new_low = rgc_basis_low(g, h, r, norm)
    assert list(h_new.shape) == [100, O]
    assert list(h_new_low.shape) == [100, O]
    assert F.allclose(h_new, h_new_low)

    if O % B == 0:
        rgc_bdd = nn.RelGraphConv(I, O, R, "bdd", B)
        rgc_bdd_low = nn.RelGraphConv(I, O, R, "bdd", B, low_mem=True)
        rgc_bdd_low.weight = rgc_bdd.weight
        rgc_bdd_low.loop_weight = rgc_bdd.loop_weight
        h = tf.random.normal((100, I))
        r = tf.constant(etype)
        h_new = rgc_bdd(g, h, r, norm)
        h_new_low = rgc_bdd_low(g, h, r, norm)
        assert list(h_new.shape) == [100, O]
        assert list(h_new_low.shape) == [100, O]
        assert F.allclose(h_new, h_new_low)

    # id input
    rgc_basis = nn.RelGraphConv(I, O, R, "basis", B)
    rgc_basis_low = nn.RelGraphConv(I, O, R, "basis", B, low_mem=True)
    rgc_basis_low.weight = rgc_basis.weight
    rgc_basis_low.w_comp = rgc_basis.w_comp
    rgc_basis_low.loop_weight = rgc_basis.loop_weight
    h = tf.constant(np.random.randint(0, I, (100,))) * 1
    r = tf.constant(etype) * 1
    h_new = rgc_basis(g, h, r)
    h_new_low = rgc_basis_low(g, h, r)
    assert list(h_new.shape) == [100, O]
    assert list(h_new_low.shape) == [100, O]
    assert F.allclose(h_new, h_new_low)


@parametrize_idtype
@pytest.mark.parametrize(
    "g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
@pytest.mark.parametrize("out_dim", [1, 2])
@pytest.mark.parametrize("num_heads", [1, 4])
def test_gat_conv(g, idtype, out_dim, num_heads):
    g = g.astype(idtype).to(F.ctx())
    ctx = F.ctx()
    gat = nn.GATConv(5, out_dim, num_heads)
    feat = F.randn((g.number_of_src_nodes(), 5))
    h = gat(g, feat)
    assert h.shape == (g.number_of_dst_nodes(), num_heads, out_dim)
    _, a = gat(g, feat, get_attention=True)
    assert a.shape == (g.number_of_edges(), num_heads, 1)

    # test residual connection
    gat = nn.GATConv(5, out_dim, num_heads, residual=True)
    h = gat(g, feat)


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["bipartite"], exclude=["zero-degree"]))
@pytest.mark.parametrize("out_dim", [1, 2])
@pytest.mark.parametrize("num_heads", [1, 4])
def test_gat_conv_bi(g, idtype, out_dim, num_heads):
    g = g.astype(idtype).to(F.ctx())
    ctx = F.ctx()
    gat = nn.GATConv(5, out_dim, num_heads)
    feat = (
        F.randn((g.number_of_src_nodes(), 5)),
        F.randn((g.number_of_dst_nodes(), 5)),
    )
    h = gat(g, feat)
    assert h.shape == (g.number_of_dst_nodes(), num_heads, out_dim)
    _, a = gat(g, feat, get_attention=True)
    assert a.shape == (g.number_of_edges(), num_heads, 1)


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["homo", "block-bipartite"]))
@pytest.mark.parametrize("aggre_type", ["mean", "pool", "gcn"])
@pytest.mark.parametrize("out_dim", [1, 10])
def test_sage_conv(idtype, g, aggre_type, out_dim):
    g = g.astype(idtype).to(F.ctx())
    sage = nn.SAGEConv(5, out_dim, aggre_type)
    feat = F.randn((g.number_of_src_nodes(), 5))
    h = sage(g, feat)
    assert h.shape[-1] == out_dim


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["bipartite"]))
@pytest.mark.parametrize("aggre_type", ["mean", "pool", "gcn"])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_sage_conv_bi(idtype, g, aggre_type, out_dim):
    g = g.astype(idtype).to(F.ctx())
    dst_dim = 5 if aggre_type != "gcn" else 10
    sage = nn.SAGEConv((10, dst_dim), out_dim, aggre_type)
    feat = (
        F.randn((g.number_of_src_nodes(), 10)),
        F.randn((g.number_of_dst_nodes(), dst_dim)),
    )
    h = sage(g, feat)
    assert h.shape[-1] == out_dim
    assert h.shape[0] == g.number_of_dst_nodes()


@parametrize_idtype
@pytest.mark.parametrize("aggre_type", ["mean", "pool", "gcn"])
@pytest.mark.parametrize("out_dim", [1, 2])
def test_sage_conv_bi_empty(idtype, aggre_type, out_dim):
    # Test the case for graphs without edges
    g = dgl.heterograph({("_U", "_E", "_V"): ([], [])}, {"_U": 5, "_V": 3}).to(
        F.ctx()
    )
    g = g.astype(idtype).to(F.ctx())
    sage = nn.SAGEConv((3, 3), out_dim, "gcn")
    feat = (F.randn((5, 3)), F.randn((3, 3)))
    h = sage(g, feat)
    assert h.shape[-1] == out_dim
    assert h.shape[0] == 3
    for aggre_type in ["mean", "pool", "lstm"]:
        sage = nn.SAGEConv((3, 1), out_dim, aggre_type)
        feat = (F.randn((5, 3)), F.randn((3, 1)))
        h = sage(g, feat)
        assert h.shape[-1] == out_dim
        assert h.shape[0] == 3


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["homo"], exclude=["zero-degree"]))
@pytest.mark.parametrize("out_dim", [1, 2])
def test_sgc_conv(g, idtype, out_dim):
    ctx = F.ctx()
    g = g.astype(idtype).to(ctx)
    # not cached
    sgc = nn.SGConv(5, out_dim, 3)
    feat = F.randn((g.number_of_nodes(), 5))

    h = sgc(g, feat)
    assert h.shape[-1] == out_dim

    # cached
    sgc = nn.SGConv(5, out_dim, 3, True)
    h_0 = sgc(g, feat)
    h_1 = sgc(g, feat + 1)
    assert F.allclose(h_0, h_1)
    assert h_0.shape[-1] == out_dim


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["homo"], exclude=["zero-degree"]))
def test_appnp_conv(g, idtype):
    ctx = F.ctx()
    g = g.astype(idtype).to(ctx)
    appnp = nn.APPNPConv(10, 0.1)
    feat = F.randn((g.number_of_nodes(), 5))

    h = appnp(g, feat)
    assert h.shape[-1] == 5


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["homo", "block-bipartite"]))
@pytest.mark.parametrize("aggregator_type", ["mean", "max", "sum"])
def test_gin_conv(g, idtype, aggregator_type):
    g = g.astype(idtype).to(F.ctx())
    ctx = F.ctx()
    gin = nn.GINConv(tf.keras.layers.Dense(12), aggregator_type)
    feat = F.randn((g.number_of_src_nodes(), 5))
    h = gin(g, feat)
    assert h.shape == (g.number_of_dst_nodes(), 12)


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["bipartite"]))
@pytest.mark.parametrize("aggregator_type", ["mean", "max", "sum"])
def test_gin_conv_bi(g, idtype, aggregator_type):
    g = g.astype(idtype).to(F.ctx())
    gin = nn.GINConv(tf.keras.layers.Dense(12), aggregator_type)
    feat = (
        F.randn((g.number_of_src_nodes(), 5)),
        F.randn((g.number_of_dst_nodes(), 5)),
    )
    h = gin(g, feat)
    assert h.shape == (g.number_of_dst_nodes(), 12)


@parametrize_idtype
@pytest.mark.parametrize(
    "g", get_cases(["homo", "block-bipartite"], exclude=["zero-degree"])
)
@pytest.mark.parametrize("out_dim", [1, 2])
def test_edge_conv(g, idtype, out_dim):
    g = g.astype(idtype).to(F.ctx())
    edge_conv = nn.EdgeConv(out_dim)

    h0 = F.randn((g.number_of_src_nodes(), 5))
    h1 = edge_conv(g, h0)
    assert h1.shape == (g.number_of_dst_nodes(), out_dim)


@parametrize_idtype
@pytest.mark.parametrize("g", get_cases(["bipartite"], exclude=["zero-degree"]))
@pytest.mark.parametrize("out_dim", [1, 2])
def test_edge_conv_bi(g, idtype, out_dim):
    g = g.astype(idtype).to(F.ctx())
    ctx = F.ctx()
    edge_conv = nn.EdgeConv(out_dim)

    h0 = F.randn((g.number_of_src_nodes(), 5))
    x0 = F.randn((g.number_of_dst_nodes(), 5))
    h1 = edge_conv(g, (h0, x0))
    assert h1.shape == (g.number_of_dst_nodes(), out_dim)


def myagg(alist, dsttype):
    rst = alist[0]
    for i in range(1, len(alist)):
        rst = rst + (i + 1) * alist[i]
    return rst


@parametrize_idtype
@pytest.mark.parametrize("agg", ["sum", "max", "min", "mean", "stack", myagg])
def test_hetero_conv(agg, idtype):
    g = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 0, 2, 1], [1, 2, 1, 3]),
            ("user", "plays", "game"): ([0, 0, 0, 1, 2], [0, 2, 3, 0, 2]),
            ("store", "sells", "game"): ([0, 0, 1, 1], [0, 3, 1, 2]),
        },
        idtype=idtype,
        device=F.ctx(),
    )
    conv = nn.HeteroGraphConv(
        {
            "follows": nn.GraphConv(2, 3, allow_zero_in_degree=True),
            "plays": nn.GraphConv(2, 4, allow_zero_in_degree=True),
            "sells": nn.GraphConv(3, 4, allow_zero_in_degree=True),
        },
        agg,
    )
    uf = F.randn((4, 2))
    gf = F.randn((4, 4))
    sf = F.randn((2, 3))

    h = conv(g, {"user": uf, "store": sf, "game": gf})
    assert set(h.keys()) == {"user", "game"}
    if agg != "stack":
        assert h["user"].shape == (4, 3)
        assert h["game"].shape == (4, 4)
    else:
        assert h["user"].shape == (4, 1, 3)
        assert h["game"].shape == (4, 2, 4)

    block = dgl.to_block(
        g.to(F.cpu()), {"user": [0, 1, 2, 3], "game": [0, 1, 2, 3], "store": []}
    ).to(F.ctx())
    h = conv(
        block,
        (
            {"user": uf, "game": gf, "store": sf},
            {"user": uf, "game": gf, "store": sf[0:0]},
        ),
    )
    assert set(h.keys()) == {"user", "game"}
    if agg != "stack":
        assert h["user"].shape == (4, 3)
        assert h["game"].shape == (4, 4)
    else:
        assert h["user"].shape == (4, 1, 3)
        assert h["game"].shape == (4, 2, 4)

    h = conv(block, {"user": uf, "game": gf, "store": sf})
    assert set(h.keys()) == {"user", "game"}
    if agg != "stack":
        assert h["user"].shape == (4, 3)
        assert h["game"].shape == (4, 4)
    else:
        assert h["user"].shape == (4, 1, 3)
        assert h["game"].shape == (4, 2, 4)

    # test with mod args
    class MyMod(tf.keras.layers.Layer):
        def __init__(self, s1, s2):
            super(MyMod, self).__init__()
            self.carg1 = 0
            self.carg2 = 0
            self.s1 = s1
            self.s2 = s2

        def call(self, g, h, arg1=None, *, arg2=None):
            if arg1 is not None:
                self.carg1 += 1
            if arg2 is not None:
                self.carg2 += 1
            return tf.zeros((g.number_of_dst_nodes(), self.s2))

    mod1 = MyMod(2, 3)
    mod2 = MyMod(2, 4)
    mod3 = MyMod(3, 4)
    conv = nn.HeteroGraphConv(
        {"follows": mod1, "plays": mod2, "sells": mod3}, agg
    )
    mod_args = {"follows": (1,), "plays": (1,)}
    mod_kwargs = {"sells": {"arg2": "abc"}}
    h = conv(
        g,
        {"user": uf, "game": gf, "store": sf},
        mod_args=mod_args,
        mod_kwargs=mod_kwargs,
    )
    assert mod1.carg1 == 1
    assert mod1.carg2 == 0
    assert mod2.carg1 == 1
    assert mod2.carg2 == 0
    assert mod3.carg1 == 0
    assert mod3.carg2 == 1

    # conv on graph without any edges
    for etype in g.etypes:
        g = dgl.remove_edges(g, g.edges(form="eid", etype=etype), etype=etype)
    assert g.num_edges() == 0
    h = conv(g, {"user": uf, "game": gf, "store": sf})
    assert set(h.keys()) == {"user", "game"}

    block = dgl.to_block(
        g.to(F.cpu()), {"user": [0, 1, 2, 3], "game": [0, 1, 2, 3], "store": []}
    ).to(F.ctx())
    h = conv(
        block,
        (
            {"user": uf, "game": gf, "store": sf},
            {"user": uf, "game": gf, "store": sf[0:0]},
        ),
    )
    assert set(h.keys()) == {"user", "game"}


@pytest.mark.parametrize("out_dim", [1, 2])
def test_dense_cheb_conv(out_dim):
    for k in range(3, 4):
        ctx = F.ctx()
        g = dgl.DGLGraph(
            sp.sparse.random(100, 100, density=0.1, random_state=42)
        )
        g = g.to(ctx)

        adj = tf.sparse.to_dense(
            tf.sparse.reorder(g.adjacency_matrix(transpose=True, ctx=ctx))
        )
        cheb = nn.ChebConv(5, out_dim, k, None, bias=True)
        dense_cheb = nn.DenseChebConv(5, out_dim, k, bias=True)

        # init cheb modules
        feat = F.ones((100, 5))
        out_cheb = cheb(g, feat, [2.0])

        dense_cheb.W = tf.reshape(cheb.linear.weights[0], (k, 5, out_dim))
        if cheb.linear.bias is not None:
            dense_cheb.bias = cheb.linear.bias

        out_dense_cheb = dense_cheb(adj, feat, 2.0)
        print(out_cheb - out_dense_cheb)
        assert F.allclose(out_cheb, out_dense_cheb)


if __name__ == "__main__":
    test_graph_conv()
    # test_set2set()
    test_glob_att_pool()
    test_simple_pool()
    # test_set_trans()
    test_rgcn()
    # test_tagconv()
    test_gat_conv()
    test_sage_conv()
    test_sgc_conv()
    test_appnp_conv()
    test_gin_conv()
    test_edge_conv()
    # test_agnn_conv()
    # test_gated_graph_conv()
    # test_nn_conv()
    # test_gmm_conv()
    # test_dense_graph_conv()
    # test_dense_sage_conv()
    test_dense_cheb_conv()
    # test_sequential()
    test_hetero_conv()