521 lines
22 KiB
Python
521 lines
22 KiB
Python
"""ProtoTorch functions test suite."""
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import unittest
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import numpy as np
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import torch
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from prototorch.functions import (activations, competitions, distances,
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initializers, losses)
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class TestActivations(unittest.TestCase):
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def setUp(self):
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self.flist = ["identity", "sigmoid_beta", "swish_beta"]
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self.x = torch.randn(1024, 1)
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def test_registry(self):
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self.assertIsNotNone(activations.ACTIVATIONS)
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def test_funcname_deserialization(self):
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for funcname in self.flist:
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f = activations.get_activation(funcname)
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iscallable = callable(f)
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self.assertTrue(iscallable)
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# def test_torch_script(self):
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# for funcname in self.flist:
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# f = activations.get_activation(funcname)
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# self.assertIsInstance(f, torch.jit.ScriptFunction)
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def test_callable_deserialization(self):
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def dummy(x, **kwargs):
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return x
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for f in [dummy, lambda x: x]:
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f = activations.get_activation(f)
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iscallable = callable(f)
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self.assertTrue(iscallable)
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self.assertEqual(1, f(1))
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def test_unknown_deserialization(self):
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for funcname in ["blubb", "foobar"]:
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with self.assertRaises(NameError):
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_ = activations.get_activation(funcname)
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def test_identity(self):
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actual = activations.identity(self.x)
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desired = self.x
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_sigmoid_beta1(self):
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actual = activations.sigmoid_beta(self.x, beta=1.0)
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desired = torch.sigmoid(self.x)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_swish_beta1(self):
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actual = activations.swish_beta(self.x, beta=1.0)
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desired = self.x * torch.sigmoid(self.x)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def tearDown(self):
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del self.x
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class TestCompetitions(unittest.TestCase):
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def setUp(self):
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pass
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def test_wtac(self):
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d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
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labels = torch.tensor([0, 1, 2, 3])
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actual = competitions.wtac(d, labels)
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desired = torch.tensor([2, 0])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_wtac_unequal_dist(self):
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d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
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labels = torch.tensor([0, 1, 1])
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actual = competitions.wtac(d, labels)
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desired = torch.tensor([0, 1])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_wtac_one_hot(self):
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d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
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labels = torch.tensor([[0, 1], [1, 0]])
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actual = competitions.wtac(d, labels)
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desired = torch.tensor([[0, 1], [1, 0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_min(self):
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d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
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labels = torch.tensor([0, 0, 1, 2])
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actual = competitions.stratified_min(d, labels)
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desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_min_one_hot(self):
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d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
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labels = torch.tensor([0, 0, 1, 2])
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labels = torch.eye(3)[labels]
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actual = competitions.stratified_min(d, labels)
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desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_min_simple(self):
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d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
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labels = torch.tensor([0, 1, 2])
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actual = competitions.stratified_min(d, labels)
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desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_knnc_k1(self):
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d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
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labels = torch.tensor([0, 1, 2, 3])
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actual = competitions.knnc(d, labels, k=1)
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desired = torch.tensor([2, 0])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def tearDown(self):
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pass
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class TestDistances(unittest.TestCase):
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def setUp(self):
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self.nx, self.mx = 32, 2048
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self.ny, self.my = 8, 2048
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self.x = torch.randn(self.nx, self.mx)
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self.y = torch.randn(self.ny, self.my)
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def test_manhattan(self):
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actual = distances.lpnorm_distance(self.x, self.y, p=1)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=1,
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keepdim=False,
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)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=2)
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self.assertIsNone(mismatch)
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def test_euclidean(self):
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actual = distances.euclidean_distance(self.x, self.y)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=2,
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keepdim=False,
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)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=3)
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self.assertIsNone(mismatch)
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def test_squared_euclidean(self):
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actual = distances.squared_euclidean_distance(self.x, self.y)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = (torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=2,
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keepdim=False,
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)**2)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=2)
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self.assertIsNone(mismatch)
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def test_lpnorm_p0(self):
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actual = distances.lpnorm_distance(self.x, self.y, p=0)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=0,
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keepdim=False,
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)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=4)
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self.assertIsNone(mismatch)
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def test_lpnorm_p2(self):
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actual = distances.lpnorm_distance(self.x, self.y, p=2)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=2,
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keepdim=False,
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)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=4)
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self.assertIsNone(mismatch)
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def test_lpnorm_p3(self):
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actual = distances.lpnorm_distance(self.x, self.y, p=3)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=3,
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keepdim=False,
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)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=4)
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self.assertIsNone(mismatch)
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def test_lpnorm_pinf(self):
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actual = distances.lpnorm_distance(self.x, self.y, p=float("inf"))
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=float("inf"),
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keepdim=False,
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)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=4)
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self.assertIsNone(mismatch)
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def test_omega_identity(self):
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omega = torch.eye(self.mx, self.my)
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actual = distances.omega_distance(self.x, self.y, omega=omega)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = (torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=2,
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keepdim=False,
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)**2)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=2)
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self.assertIsNone(mismatch)
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def test_lomega_identity(self):
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omega = torch.eye(self.mx, self.my)
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omegas = torch.stack([omega for _ in range(self.ny)], dim=0)
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actual = distances.lomega_distance(self.x, self.y, omegas=omegas)
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desired = torch.empty(self.nx, self.ny)
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for i in range(self.nx):
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for j in range(self.ny):
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desired[i][j] = (torch.nn.functional.pairwise_distance(
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self.x[i].reshape(1, -1),
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self.y[j].reshape(1, -1),
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p=2,
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keepdim=False,
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)**2)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=2)
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self.assertIsNone(mismatch)
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def tearDown(self):
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del self.x, self.y
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class TestInitializers(unittest.TestCase):
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def setUp(self):
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self.flist = [
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"zeros",
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"ones",
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"rand",
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"randn",
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"stratified_mean",
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"stratified_random",
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]
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self.x = torch.tensor(
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[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
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dtype=torch.float32)
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self.y = torch.tensor([0, 0, 1, 1])
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self.gen = torch.manual_seed(42)
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def test_registry(self):
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self.assertIsNotNone(initializers.INITIALIZERS)
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def test_funcname_deserialization(self):
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for funcname in self.flist:
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f = initializers.get_initializer(funcname)
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iscallable = callable(f)
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self.assertTrue(iscallable)
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def test_callable_deserialization(self):
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def dummy(x):
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return x
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for f in [dummy, lambda x: x]:
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f = initializers.get_initializer(f)
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iscallable = callable(f)
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self.assertTrue(iscallable)
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self.assertEqual(1, f(1))
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def test_unknown_deserialization(self):
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for funcname in ["blubb", "foobar"]:
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with self.assertRaises(NameError):
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_ = initializers.get_initializer(funcname)
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def test_zeros(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.zeros(self.x, self.y, pdist)
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desired = torch.zeros(2, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_ones(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.ones(self.x, self.y, pdist)
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desired = torch.ones(2, 3)
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_rand(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.rand(self.x, self.y, pdist)
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desired = torch.rand(2, 3, generator=torch.manual_seed(42))
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_randn(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.randn(self.x, self.y, pdist)
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desired = torch.randn(2, 3, generator=torch.manual_seed(42))
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_mean_equal1(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
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desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_random_equal1(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.stratified_random(self.x, self.y, pdist,
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False)
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desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_mean_equal2(self):
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pdist = torch.tensor([2, 2])
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
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desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
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[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_random_equal2(self):
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pdist = torch.tensor([2, 2])
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actual, _ = initializers.stratified_random(self.x, self.y, pdist,
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False)
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desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
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[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_mean_unequal(self):
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pdist = torch.tensor([1, 3])
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
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desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
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[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_random_unequal(self):
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pdist = torch.tensor([1, 3])
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actual, _ = initializers.stratified_random(self.x, self.y, pdist,
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False)
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desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_mean_unequal_one_hot(self):
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pdist = torch.tensor([1, 3])
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y = torch.eye(2)[self.y]
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desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
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|
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
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|
actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
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desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
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|
mismatch = np.testing.assert_array_almost_equal(actual1,
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|
desired1,
|
|
decimal=5)
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|
mismatch = np.testing.assert_array_almost_equal(actual2,
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|
desired2,
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|
decimal=5)
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|
self.assertIsNone(mismatch)
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|
|
|
def test_stratified_random_unequal_one_hot(self):
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|
pdist = torch.tensor([1, 3])
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|
y = torch.eye(2)[self.y]
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|
actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
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|
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):
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|
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
|