Add core test suite

tests/test_core.py

@@ -0,0 +1,552 @@
+"""ProtoTorch core test suite"""
+
+import unittest
+
+import numpy as np
+import pytest
+import torch
+
+import prototorch as pt
+from prototorch.utils import parse_distribution
+
+
+# Utils
+def test_parse_distribution_dict_0():
+    distribution = {"num_classes": 1, "per_class": 0}
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 0}
+
+
+def test_parse_distribution_dict_1():
+    distribution = dict(num_classes=3, per_class=2)
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 2, 1: 2, 2: 2}
+
+
+def test_parse_distribution_dict_2():
+    distribution = {0: 1, 2: 2, -1: 3}
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 1, 2: 2, -1: 3}
+
+
+def test_parse_distribution_tuple():
+    distribution = (2, 3)
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 3, 1: 3}
+
+
+def test_parse_distribution_list():
+    distribution = [1, 1, 0, 2]
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 1, 1: 1, 2: 0, 3: 2}
+
+
+# Components initializers
+def test_shape_aware_raises_error():
+    with pytest.raises(TypeError):
+        _ = pt.initializers.ShapeAwareCompInitializer(shape=(2, ))
+
+
+def test_literal_comp_generate():
+    protos = torch.rand(4, 3, 5, 5)
+    c = pt.initializers.LiteralCompInitializer(protos)
+    components = c.generate(num_components="IgnoreMe!")
+    assert torch.allclose(components, protos)
+
+
+def test_zeros_comp_generate():
+    shape = (3, 5, 5)
+    c = pt.initializers.ZerosCompInitializer(shape)
+    components = c.generate(num_components=4)
+    assert torch.allclose(components, torch.zeros(4, 3, 5, 5))
+
+
+def test_ones_comp_generate():
+    c = pt.initializers.OnesCompInitializer(2)
+    components = c.generate(num_components=3)
+    assert torch.allclose(components, torch.ones(3, 2))
+
+
+def test_fill_value_comp_generate():
+    c = pt.initializers.FillValueCompInitializer(2, 0.0)
+    components = c.generate(num_components=3)
+    assert torch.allclose(components, torch.zeros(3, 2))
+
+
+def test_comp_generate_0_components():
+    c = pt.initializers.ZerosCompInitializer(2)
+    _ = c.generate(num_components=0)
+
+
+def test_stratified_mean_comp_generate():
+    # yapf: disable
+    x = torch.Tensor(
+        [[0,  -1, -2],
+         [10, 11, 12],
+         [0,   0,  0],
+         [2,   2,  2]])
+    y = torch.LongTensor([0, 0, 1, 1])
+    desired = torch.Tensor(
+        [[5.0, 5.0, 5.0],
+         [1.0, 1.0, 1.0]])
+    # yapf: enable
+    c = pt.initializers.StratifiedMeanCompInitializer(data=[x, y])
+    actual = c.generate([1, 1])
+    assert torch.allclose(actual, desired)
+
+
+def test_stratified_selection_comp_generate():
+    # yapf: disable
+    x = torch.Tensor(
+        [[0, 0, 0],
+         [1, 1, 1],
+         [0, 0, 0],
+         [1, 1, 1]])
+    y = torch.LongTensor([0, 1, 0, 1])
+    desired = torch.Tensor(
+        [[0, 0, 0],
+         [1, 1, 1]])
+    # yapf: enable
+    c = pt.initializers.StratifiedSelectionCompInitializer(data=[x, y])
+    actual = c.generate([1, 1])
+    assert torch.allclose(actual, desired)
+
+
+# Labels initializers
+def test_labels_init_from_list():
+    l = pt.initializers.LabelsInitializer()
+    components = l.generate(distribution=[1, 1, 1])
+    assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
+
+
+def test_labels_init_from_tuple_legal():
+    l = pt.initializers.LabelsInitializer()
+    components = l.generate(distribution=(3, 1))
+    assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
+
+
+def test_labels_init_from_tuple_illegal():
+    l = pt.initializers.LabelsInitializer()
+    with pytest.raises(AssertionError):
+        _ = l.generate(distribution=(1, 1, 1))
+
+
+# Components
+def test_components_no_initializer():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components(3, None)
+
+
+def test_components_no_num_components():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components(initializer=pt.initializers.OCI(2))
+
+
+def test_components_none_num_components():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components(None, initializer=pt.initializers.OCI(2))
+
+
+def test_components_no_args():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components()
+
+
+def test_components_zeros_init():
+    c = pt.components.Components(3, pt.initializers.ZCI(2))
+    assert torch.allclose(c.components, torch.zeros(3, 2))
+
+
+# Losses
+def test_glvq_loss_int_labels():
+    d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+    labels = torch.tensor([0, 1])
+    targets = torch.ones(100)
+    batch_loss = pt.losses.glvq_loss(distances=d,
+                                     target_labels=targets,
+                                     prototype_labels=labels)
+    loss_value = torch.sum(batch_loss, dim=0)
+    assert loss_value == -100
+
+
+def test_glvq_loss_one_hot_labels():
+    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 = pt.losses.glvq_loss(distances=d,
+                                     target_labels=targets,
+                                     prototype_labels=labels)
+    loss_value = torch.sum(batch_loss, dim=0)
+    assert loss_value == -100
+
+
+def test_glvq_loss_one_hot_unequal():
+    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 = pt.losses.glvq_loss(distances=d,
+                                     target_labels=targets,
+                                     prototype_labels=labels)
+    loss_value = torch.sum(batch_loss, dim=0)
+    assert loss_value == -100
+
+
+# Activations
+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(pt.nn.ACTIVATIONS)
+
+    def test_funcname_deserialization(self):
+        for funcname in self.flist:
+            f = pt.nn.get_activation(funcname)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+
+    def test_callable_deserialization(self):
+        def dummy(x, **kwargs):
+            return x
+
+        for f in [dummy, lambda x: x]:
+            f = pt.nn.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):
+                _ = pt.nn.get_activation(funcname)
+
+    def test_identity(self):
+        actual = pt.nn.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 = pt.nn.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 = pt.nn.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
+
+
+# Competitions
+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 = pt.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 = pt.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 = pt.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 = pt.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
+
+
+# Pooling
+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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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
+
+
+# Distances
+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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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 = pt.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