522 lines
22 KiB
Python
522 lines
22 KiB
Python
"""ProtoTorch functions test suite."""
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
from prototorch.functions import (activations, competitions, distances,
|
|
initializers, losses)
|
|
|
|
|
|
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=torch.tensor(1))
|
|
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=torch.tensor(1))
|
|
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_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 = competitions.stratified_min(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 = competitions.stratified_min(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_simple(self):
|
|
d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
|
|
labels = torch.tensor([0, 1, 2])
|
|
actual = competitions.stratified_min(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_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=torch.tensor([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 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
|