decagon-pytorch/tests/icosagon/test_model.py

from icosagon.data import Data, \
    _equal
from icosagon.model import Model
from icosagon.trainprep import PreparedData, \
    PreparedRelationFamily, \
    PreparedRelationType, \
    TrainValTest, \
    norm_adj_mat_one_node_type
import torch
from icosagon.input import OneHotInputLayer
from icosagon.convlayer import DecagonLayer
from icosagon.declayer import DecodeLayer


def _is_identity_function(f):
    for x in range(-100, 101):
        if f(x) != x:
            return False
    return True


def test_model_01():
    d = Data()
    d.add_node_type('Dummy', 10)
    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
    fam.add_relation_type('Dummy Rel', torch.rand(10, 10).round())

    m = Model(d)

    assert m.data == d
    assert m.layer_dimensions == [32, 64]
    assert (m.ratios.train, m.ratios.val, m.ratios.test) == (.8, .1, .1)
    assert m.keep_prob == 1.
    assert _is_identity_function(m.rel_activation)
    assert m.layer_activation == torch.nn.functional.relu
    assert _is_identity_function(m.dec_activation)
    assert m.lr == 0.001
    assert m.loss == torch.nn.functional.binary_cross_entropy_with_logits
    assert m.batch_size == 100
    assert isinstance(m.prep_d, PreparedData)
    assert isinstance(m.seq, torch.nn.Sequential)
    assert isinstance(m.opt, torch.optim.Optimizer)


def test_model_02():
    d = Data()
    d.add_node_type('Dummy', 10)
    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
    mat = torch.rand(10, 10).round().to_sparse()
    fam.add_relation_type('Dummy Rel', mat)

    m = Model(d, ratios=TrainValTest(1., 0., 0.))

    assert isinstance(m.prep_d, PreparedData)
    assert isinstance(m.prep_d.relation_families, list)
    assert len(m.prep_d.relation_families) == 1
    assert isinstance(m.prep_d.relation_families[0], PreparedRelationFamily)
    assert len(m.prep_d.relation_families[0].relation_types) == 1
    assert isinstance(m.prep_d.relation_families[0].relation_types[0], PreparedRelationType)
    assert m.prep_d.relation_families[0].relation_types[0].adjacency_matrix_backward is None
    assert torch.all(_equal(m.prep_d.relation_families[0].relation_types[0].adjacency_matrix,
        norm_adj_mat_one_node_type(mat)))

    assert isinstance(m.seq[0], OneHotInputLayer)
    assert isinstance(m.seq[1], DecagonLayer)
    assert isinstance(m.seq[2], DecagonLayer)
    assert isinstance(m.seq[3], DecodeLayer)
    assert len(m.seq) == 4


def test_model_03():
    d = Data()
    d.add_node_type('Dummy', 10)
    fam = d.add_relation_family('Dummy-Dummy', 0, 0, False)
    mat = torch.rand(10, 10).round().to_sparse()
    fam.add_relation_type('Dummy Rel', mat)

    m = Model(d, ratios=TrainValTest(1., 0., 0.))

    state_dict = m.opt.state_dict()
    assert isinstance(state_dict, dict)
    # print(state_dict['param_groups'])
    # print(list(m.seq.parameters()))
    assert len(list(m.seq[0].parameters())) == 1
    assert len(list(m.seq[1].parameters())) == 1
    assert len(list(m.seq[2].parameters())) == 1
    assert len(list(m.seq[3].parameters())) == 2
    # print(list(m.seq[1].parameters()))