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Remove tests/test_functions.py

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Jensun Ravichandran 2021-06-12 20:30:53 +02:00
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tests/test_functions.py
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"""ProtoTorch functions test suite."""
import unittest
import numpy as np
import torch
from prototorch.functions import (activations, competitions, distances,
initializers, losses, pooling)
class TestActivations(unittest.TestCase):
def setUp(self):
self.flist = ["identity", "sigmoid_beta", "swish_beta"]
self.x = torch.randn(1024, 1)
def test_registry(self):
self.assertIsNotNone(activations.ACTIVATIONS)
def test_funcname_deserialization(self):
for funcname in self.flist:
f = activations.get_activation(funcname)
iscallable = callable(f)
self.assertTrue(iscallable)
# def test_torch_script(self):
# for funcname in self.flist:
# f = activations.get_activation(funcname)
# self.assertIsInstance(f, torch.jit.ScriptFunction)
def test_callable_deserialization(self):
def dummy(x, **kwargs):
return x
for f in [dummy, lambda x: x]:
f = activations.get_activation(f)
iscallable = callable(f)
self.assertTrue(iscallable)
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = activations.get_activation(funcname)
def test_identity(self):
actual = activations.identity(self.x)
desired = self.x
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_sigmoid_beta1(self):
actual = activations.sigmoid_beta(self.x, beta=1.0)
desired = torch.sigmoid(self.x)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_swish_beta1(self):
actual = activations.swish_beta(self.x, beta=1.0)
desired = self.x * torch.sigmoid(self.x)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self):
del self.x
class TestCompetitions(unittest.TestCase):
def setUp(self):
pass
def test_wtac(self):
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.wtac(d, labels)
desired = torch.tensor([2, 0])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_wtac_unequal_dist(self):
d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
labels = torch.tensor([0, 1, 1])
actual = competitions.wtac(d, labels)
desired = torch.tensor([0, 1])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_wtac_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
actual = competitions.wtac(d, labels)
desired = torch.tensor([[0, 1], [1, 0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=1)
desired = torch.tensor([2, 0])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self):
pass
class TestPooling(unittest.TestCase):
def setUp(self):
pass
def test_stratified_min(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
actual = pooling.stratified_min_pooling(d, labels)
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_one_hot(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels]
actual = pooling.stratified_min_pooling(d, labels)
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_trivial(self):
d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
labels = torch.tensor([0, 1, 2])
actual = pooling.stratified_min_pooling(d, labels)
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_max(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
labels = torch.tensor([0, 0, 3, 2, 0])
actual = pooling.stratified_max_pooling(d, labels)
desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_max_one_hot(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
labels = torch.tensor([0, 0, 2, 1, 0])
labels = torch.nn.functional.one_hot(labels, num_classes=3)
actual = pooling.stratified_max_pooling(d, labels)
desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_sum(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.LongTensor([0, 0, 1, 2])
actual = pooling.stratified_sum_pooling(d, labels)
desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_sum_one_hot(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels]
actual = pooling.stratified_sum_pooling(d, labels)
desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_prod(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
labels = torch.tensor([0, 0, 3, 2, 0])
actual = pooling.stratified_prod_pooling(d, labels)
desired = torch.tensor([[0.0, 3.0, 2.0], [504.0, 1.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self):
pass
class TestDistances(unittest.TestCase):
def setUp(self):
self.nx, self.mx = 32, 2048
self.ny, self.my = 8, 2048
self.x = torch.randn(self.nx, self.mx)
self.y = torch.randn(self.ny, self.my)
def test_manhattan(self):
actual = distances.lpnorm_distance(self.x, self.y, p=1)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=1,
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
self.assertIsNone(mismatch)
def test_euclidean(self):
actual = distances.euclidean_distance(self.x, self.y)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=3)
self.assertIsNone(mismatch)
def test_squared_euclidean(self):
actual = distances.squared_euclidean_distance(self.x, self.y)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
self.assertIsNone(mismatch)
def test_lpnorm_p0(self):
actual = distances.lpnorm_distance(self.x, self.y, p=0)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=0,
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=4)
self.assertIsNone(mismatch)
def test_lpnorm_p2(self):
actual = distances.lpnorm_distance(self.x, self.y, p=2)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=4)
self.assertIsNone(mismatch)
def test_lpnorm_p3(self):
actual = distances.lpnorm_distance(self.x, self.y, p=3)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=3,
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=4)
self.assertIsNone(mismatch)
def test_lpnorm_pinf(self):
actual = distances.lpnorm_distance(self.x, self.y, p=float("inf"))
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=float("inf"),
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=4)
self.assertIsNone(mismatch)
def test_omega_identity(self):
omega = torch.eye(self.mx, self.my)
actual = distances.omega_distance(self.x, self.y, omega=omega)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
self.assertIsNone(mismatch)
def test_lomega_identity(self):
omega = torch.eye(self.mx, self.my)
omegas = torch.stack([omega for _ in range(self.ny)], dim=0)
actual = distances.lomega_distance(self.x, self.y, omegas=omegas)
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
self.assertIsNone(mismatch)
def tearDown(self):
del self.x, self.y
class TestInitializers(unittest.TestCase):
def setUp(self):
self.flist = [
"zeros",
"ones",
"rand",
"randn",
"stratified_mean",
"stratified_random",
]
self.x = torch.tensor(
[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
dtype=torch.float32)
self.y = torch.tensor([0, 0, 1, 1])
self.gen = torch.manual_seed(42)
def test_registry(self):
self.assertIsNotNone(initializers.INITIALIZERS)
def test_funcname_deserialization(self):
for funcname in self.flist:
f = initializers.get_initializer(funcname)
iscallable = callable(f)
self.assertTrue(iscallable)
def test_callable_deserialization(self):
def dummy(x):
return x
for f in [dummy, lambda x: x]:
f = initializers.get_initializer(f)
iscallable = callable(f)
self.assertTrue(iscallable)
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = initializers.get_initializer(funcname)
def test_zeros(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.zeros(self.x, self.y, pdist)
desired = torch.zeros(2, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_ones(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.ones(self.x, self.y, pdist)
desired = torch.ones(2, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_rand(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.rand(self.x, self.y, pdist)
desired = torch.rand(2, 3, generator=torch.manual_seed(42))
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_randn(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.randn(self.x, self.y, pdist)
desired = torch.randn(2, 3, generator=torch.manual_seed(42))
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_mean_equal1(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_random_equal1(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_mean_equal2(self):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_random_equal2(self):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_mean_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_random_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_mean_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,
decimal=5)
mismatch = np.testing.assert_array_almost_equal(actual2,
desired2,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_random_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
desired1 = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,
decimal=5)
mismatch = np.testing.assert_array_almost_equal(actual2,
desired2,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self):
del self.x, self.y, self.gen
_ = torch.seed()
class TestLosses(unittest.TestCase):
def setUp(self):
pass
def test_glvq_loss_int_labels(self):
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([0, 1])
targets = torch.ones(100)
batch_loss = losses.glvq_loss(distances=d,
target_labels=targets,
prototype_labels=labels)
loss_value = torch.sum(batch_loss, dim=0)
self.assertEqual(loss_value, -100)
def test_glvq_loss_one_hot_labels(self):
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([[0, 1], [1, 0]])
wl = torch.tensor([1, 0])
targets = torch.stack([wl for _ in range(100)], dim=0)
batch_loss = losses.glvq_loss(distances=d,
target_labels=targets,
prototype_labels=labels)
loss_value = torch.sum(batch_loss, dim=0)
self.assertEqual(loss_value, -100)
def test_glvq_loss_one_hot_unequal(self):
dlist = [torch.ones(100), torch.zeros(100), torch.zeros(100)]
d = torch.stack(dlist, dim=1)
labels = torch.tensor([[0, 1], [1, 0], [1, 0]])
wl = torch.tensor([1, 0])
targets = torch.stack([wl for _ in range(100)], dim=0)
batch_loss = losses.glvq_loss(distances=d,
target_labels=targets,
prototype_labels=labels)
loss_value = torch.sum(batch_loss, dim=0)
self.assertEqual(loss_value, -100)
def tearDown(self):
pass