decagon-pytorch/tests/icosagon/test_fastconv.py

from icosagon.fastconv import _sparse_diag_cat, \
    _cat, \
    FastGraphConv
from icosagon.data import _equal
import torch
import pdb
import time


def test_sparse_diag_cat_01():
    matrices = [ torch.rand(5, 10).round() for _ in range(7) ]
    ground_truth = torch.zeros(35, 70)
    ground_truth[0:5, 0:10] = matrices[0]
    ground_truth[5:10, 10:20] = matrices[1]
    ground_truth[10:15, 20:30] = matrices[2]
    ground_truth[15:20, 30:40] = matrices[3]
    ground_truth[20:25, 40:50] = matrices[4]
    ground_truth[25:30, 50:60] = matrices[5]
    ground_truth[30:35, 60:70] = matrices[6]
    res = _sparse_diag_cat([ m.to_sparse() for m in matrices ])
    res = res.to_dense()
    assert torch.all(res == ground_truth)


def test_sparse_diag_cat_02():
    x = [ torch.rand(5, 10).round() for _ in range(7) ]
    a = [ m.to_sparse() for m in x ]
    a = _sparse_diag_cat(a)
    b = torch.rand(70, 64)
    res = torch.sparse.mm(a, b)

    ground_truth = torch.zeros(35, 64)
    ground_truth[0:5, :] = torch.mm(x[0], b[0:10])
    ground_truth[5:10, :] = torch.mm(x[1], b[10:20])
    ground_truth[10:15, :] = torch.mm(x[2], b[20:30])
    ground_truth[15:20, :] = torch.mm(x[3], b[30:40])
    ground_truth[20:25, :] = torch.mm(x[4], b[40:50])
    ground_truth[25:30, :] = torch.mm(x[5], b[50:60])
    ground_truth[30:35, :] = torch.mm(x[6], b[60:70])

    assert torch.all(res == ground_truth)


def test_cat_01():
    matrices = [ torch.rand(5, 10) for _ in range(7) ]
    res = _cat(matrices)
    assert res.shape == (35, 10)
    assert not res.is_sparse
    ground_truth = torch.zeros(35, 10)
    for i in range(7):
        ground_truth[i*5:(i+1)*5, :] = matrices[i]
    assert torch.all(res == ground_truth)


def test_cat_02():
    matrices = [ torch.rand(5, 10) for _ in range(7) ]
    ground_truth = torch.zeros(35, 10)
    for i in range(7):
        ground_truth[i*5:(i+1)*5, :] = matrices[i]
    res = _cat([ m.to_sparse() for m in matrices ])
    assert res.shape == (35, 10)
    assert res.is_sparse
    assert torch.all(res.to_dense() == ground_truth)


def test_fast_graph_conv_01():
    # pdb.set_trace()
    adj_mats = [ torch.rand(10, 15).round().to_sparse() \
        for _ in range(23) ]
    fgc = FastGraphConv(32, 64, adj_mats)
    in_repr = torch.rand(15, 32)
    _ = fgc(in_repr)


def test_fast_graph_conv_02():
    t = time.time()
    m = (torch.rand(2000, 2000) < .001).to(torch.float32).to_sparse()
    adj_mats = [ m for _ in range(1300) ]
    print('Generating adj_mats took:', time.time() - t)
    t = time.time()
    fgc = FastGraphConv(32, 64, adj_mats)
    print('FGC constructor took:', time.time() - t)
    in_repr = torch.rand(2000, 32)

    for _ in range(3):
        t = time.time()
        _ = fgc(in_repr)
        print('FGC forward pass took:', time.time() - t)