/tests/go/test_model.py

import dgl
import pytest
import torch
from dglgo.model import *
from test_utils.graph_cases import get_cases

@pytest.mark.parametrize('g', get_cases(['has_scalar_e_feature']))
def test_gcn(g):
    data_info = {
        'num_nodes': g.num_nodes(),
        'out_size': 7
    }
    node_feat = None
    edge_feat = g.edata['scalar_w']

    # node embedding + not use_edge_weight
    model = GCN(data_info, embed_size=10, use_edge_weight=False)
    model(g, node_feat)

    # node embedding + use_edge_weight
    model = GCN(data_info, embed_size=10, use_edge_weight=True)
    model(g, node_feat, edge_feat)

    data_info['in_size'] = g.ndata['h'].shape[-1]
    node_feat = g.ndata['h']

    # node feat + not use_edge_weight
    model = GCN(data_info, embed_size=-1, use_edge_weight=False)
    model(g, node_feat)

    # node feat + use_edge_weight
    model = GCN(data_info, embed_size=-1, use_edge_weight=True)
    model(g, node_feat, edge_feat)

@pytest.mark.parametrize('g', get_cases(['block-bipartite']))
def test_gcn_block(g):
    data_info = {
        'in_size': 10,
        'out_size': 7
    }

    blocks = [g]
    node_feat = torch.randn(g.num_src_nodes(), data_info['in_size'])
    edge_feat = torch.abs(torch.randn(g.num_edges()))
    # not use_edge_weight
    model = GCN(data_info, use_edge_weight=False)
    model.forward_block(blocks, node_feat)

    # use_edge_weight
    model = GCN(data_info, use_edge_weight=True)
    model.forward_block(blocks, node_feat, edge_feat)

@pytest.mark.parametrize('g', get_cases(['has_scalar_e_feature']))
def test_gat(g):
    data_info = {
        'num_nodes': g.num_nodes(),
        'out_size': 7
    }
    node_feat = None

    # node embedding
    model = GAT(data_info, embed_size=10)
    model(g, node_feat)

    # node feat
    data_info['in_size'] = g.ndata['h'].shape[-1]
    node_feat = g.ndata['h']
    model = GAT(data_info, embed_size=-1)
    model(g, node_feat)

@pytest.mark.parametrize('g', get_cases(['block-bipartite']))
def test_gat_block(g):
    data_info = {
        'in_size': 10,
        'out_size': 7
    }

    blocks = [g]
    node_feat = torch.randn(g.num_src_nodes(), data_info['in_size'])
    model = GAT(data_info, num_layers=1, heads=[8])
    model.forward_block(blocks, node_feat)

@pytest.mark.parametrize('g', get_cases(['has_scalar_e_feature']))
def test_gin(g):
    data_info = {
        'num_nodes': g.num_nodes(),
        'out_size': 7
    }
    node_feat = None

    # node embedding
    model = GIN(data_info, embed_size=10)
    model(g, node_feat)

    # node feat
    data_info['in_size'] = g.ndata['h'].shape[-1]
    node_feat = g.ndata['h']
    model = GIN(data_info, embed_size=-1)
    model(g, node_feat)

@pytest.mark.parametrize('g', get_cases(['has_scalar_e_feature']))
def test_sage(g):
    data_info = {
        'num_nodes': g.num_nodes(),
        'out_size': 7
    }
    node_feat = None
    edge_feat = g.edata['scalar_w']

    # node embedding
    model = GraphSAGE(data_info, embed_size=10)
    model(g, node_feat)
    model(g, node_feat, edge_feat)

    # node feat
    data_info['in_size'] = g.ndata['h'].shape[-1]
    node_feat = g.ndata['h']
    model = GraphSAGE(data_info, embed_size=-1)
    model(g, node_feat)
    model(g, node_feat, edge_feat)

@pytest.mark.parametrize('g', get_cases(['block-bipartite']))
def test_sage_block(g):
    data_info = {
        'in_size': 10,
        'out_size': 7
    }

    blocks = [g]
    node_feat = torch.randn(g.num_src_nodes(), data_info['in_size'])
    edge_feat = torch.abs(torch.randn(g.num_edges()))
    model = GraphSAGE(data_info, embed_size=-1)
    model.forward_block(blocks, node_feat)
    model.forward_block(blocks, node_feat, edge_feat)

@pytest.mark.parametrize('g', get_cases(['has_scalar_e_feature']))
def test_sgc(g):
    data_info = {
        'num_nodes': g.num_nodes(),
        'out_size': 7
    }
    node_feat = None

    # node embedding
    model = SGC(data_info, embed_size=10)
    model(g, node_feat)

    # node feat
    data_info['in_size'] = g.ndata['h'].shape[-1]
    node_feat = g.ndata['h']
    model = SGC(data_info, embed_size=-1)
    model(g, node_feat)

def test_bilinear():
    data_info = {
        'in_size': 10,
        'out_size': 1
    }
    model = BilinearPredictor(data_info)
    num_pairs = 10
    h_src = torch.randn(num_pairs, data_info['in_size'])
    h_dst = torch.randn(num_pairs, data_info['in_size'])
    model(h_src, h_dst)

def test_ele():
    data_info = {
        'in_size': 10,
        'out_size': 1
    }
    model = ElementWiseProductPredictor(data_info)
    num_pairs = 10
    h_src = torch.randn(num_pairs, data_info['in_size'])
    h_dst = torch.randn(num_pairs, data_info['in_size'])
    model(h_src, h_dst)

@pytest.mark.parametrize('virtual_node', [True, False])
def test_ogbg_gin(virtual_node):
    # Test for ogbg-mol datasets
    data_info = {
        'name': 'ogbg-molhiv',
        'out_size': 1
    }
    model = OGBGGIN(data_info,
                    embed_size=10,
                    num_layers=2,
                    virtual_node=virtual_node)
    num_nodes = 5
    num_edges = 15
    g1 = dgl.rand_graph(num_nodes, num_edges)
    g2 = dgl.rand_graph(num_nodes, num_edges)
    g = dgl.batch([g1, g2])
    num_nodes = g.num_nodes()
    num_edges = g.num_edges()
    nfeat = torch.zeros(num_nodes, 9).long()
    efeat = torch.zeros(num_edges, 3).long()
    model(g, nfeat, efeat)

    # Test for non-ogbg-mol datasets
    data_info = {
        'name': 'a_dataset',
        'out_size': 1,
        'node_feat_size': 15,
        'edge_feat_size': 5
    }
    model = OGBGGIN(data_info,
                    embed_size=10,
                    num_layers=2,
                    virtual_node=virtual_node)
    nfeat = torch.randn(num_nodes, data_info['node_feat_size'])
    efeat = torch.randn(num_edges, data_info['edge_feat_size'])
    model(g, nfeat, efeat)

def test_pna():
    # Test for ogbg-mol datasets
    data_info = {
        'name': 'ogbg-molhiv',
        'delta': 1,
        'out_size': 1
    }
    model = PNA(data_info,
                embed_size=10,
                num_layers=2)
    num_nodes = 5
    num_edges = 15
    g = dgl.rand_graph(num_nodes, num_edges)
    nfeat = torch.zeros(num_nodes, 9).long()
    model(g, nfeat)

    # Test for non-ogbg-mol datasets
    data_info = {
        'name': 'a_dataset',
        'node_feat_size': 15,
        'delta': 1,
        'out_size': 1
    }
    model = PNA(data_info,
                embed_size=10,
                num_layers=2)
    nfeat = torch.randn(num_nodes, data_info['node_feat_size'])
    model(g, nfeat)