761 lines
27 KiB
Python
761 lines
27 KiB
Python
"""ProtoTorch core test suite"""
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import unittest
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import numpy as np
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import pytest
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import torch
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import prototorch as pt
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from prototorch.utils import parse_distribution
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# Utils
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def test_parse_distribution_dict_0():
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distribution = {"num_classes": 1, "per_class": 0}
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distribution = parse_distribution(distribution)
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assert distribution == {0: 0}
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def test_parse_distribution_dict_1():
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distribution = dict(num_classes=3, per_class=2)
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distribution = parse_distribution(distribution)
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assert distribution == {0: 2, 1: 2, 2: 2}
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def test_parse_distribution_dict_2():
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distribution = {0: 1, 2: 2, -1: 3}
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distribution = parse_distribution(distribution)
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assert distribution == {0: 1, 2: 2, -1: 3}
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def test_parse_distribution_tuple():
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distribution = (2, 3)
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distribution = parse_distribution(distribution)
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assert distribution == {0: 3, 1: 3}
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def test_parse_distribution_list():
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distribution = [1, 1, 0, 2]
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distribution = parse_distribution(distribution)
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assert distribution == {0: 1, 1: 1, 2: 0, 3: 2}
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def test_parse_distribution_custom_labels():
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distribution = [1, 1, 0, 2]
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clabels = [1, 2, 5, 3]
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distribution = parse_distribution(distribution, clabels)
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assert distribution == {1: 1, 2: 1, 5: 0, 3: 2}
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# Components initializers
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def test_literal_comp_generate():
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protos = torch.rand(4, 3, 5, 5)
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c = pt.initializers.LiteralCompInitializer(protos)
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components = c.generate([])
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assert torch.allclose(components, protos)
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def test_literal_comp_generate_from_list():
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protos = [[0, 1], [2, 3], [4, 5]]
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c = pt.initializers.LiteralCompInitializer(protos)
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with pytest.warns(UserWarning):
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components = c.generate([])
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assert torch.allclose(components, torch.Tensor(protos))
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def test_shape_aware_raises_error():
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with pytest.raises(TypeError):
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_ = pt.initializers.ShapeAwareCompInitializer(shape=(2, ))
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def test_data_aware_comp_generate():
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protos = torch.rand(4, 3, 5, 5)
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c = pt.initializers.DataAwareCompInitializer(protos)
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components = c.generate(num_components="IgnoreMe!")
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assert torch.allclose(components, protos)
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def test_class_aware_comp_generate():
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protos = torch.rand(4, 2, 3, 5, 5)
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plabels = torch.tensor([0, 0, 1, 1]).long()
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c = pt.initializers.ClassAwareCompInitializer([protos, plabels])
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components = c.generate(distribution=[])
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assert torch.allclose(components, protos)
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def test_zeros_comp_generate():
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shape = (3, 5, 5)
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c = pt.initializers.ZerosCompInitializer(shape)
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components = c.generate(num_components=4)
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assert torch.allclose(components, torch.zeros(4, 3, 5, 5))
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def test_ones_comp_generate():
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c = pt.initializers.OnesCompInitializer(2)
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components = c.generate(num_components=3)
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assert torch.allclose(components, torch.ones(3, 2))
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def test_fill_value_comp_generate():
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c = pt.initializers.FillValueCompInitializer(2, 0.0)
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components = c.generate(num_components=3)
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assert torch.allclose(components, torch.zeros(3, 2))
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def test_uniform_comp_generate_min_max_bound():
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c = pt.initializers.UniformCompInitializer(2, -1.0, 1.0)
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components = c.generate(num_components=1024)
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assert components.min() >= -1.0
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assert components.max() <= 1.0
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def test_random_comp_generate_mean():
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c = pt.initializers.RandomNormalCompInitializer(2, -1.0)
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components = c.generate(num_components=1024)
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assert torch.allclose(components.mean(),
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torch.tensor(-1.0),
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rtol=1e-05,
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atol=1e-01)
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def test_comp_generate_0_components():
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c = pt.initializers.ZerosCompInitializer(2)
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_ = c.generate(num_components=0)
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def test_stratified_mean_comp_generate():
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# yapf: disable
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x = torch.Tensor(
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[[0, -1, -2],
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[10, 11, 12],
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[0, 0, 0],
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[2, 2, 2]])
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y = torch.LongTensor([0, 0, 1, 1])
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desired = torch.Tensor(
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[[5.0, 5.0, 5.0],
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[1.0, 1.0, 1.0]])
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# yapf: enable
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c = pt.initializers.StratifiedMeanCompInitializer(data=[x, y])
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actual = c.generate([1, 1])
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assert torch.allclose(actual, desired)
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def test_stratified_selection_comp_generate():
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# yapf: disable
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x = torch.Tensor(
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[[0, 0, 0],
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[1, 1, 1],
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[0, 0, 0],
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[1, 1, 1]])
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y = torch.LongTensor([0, 1, 0, 1])
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desired = torch.Tensor(
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[[0, 0, 0],
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[1, 1, 1]])
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# yapf: enable
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c = pt.initializers.StratifiedSelectionCompInitializer(data=[x, y])
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actual = c.generate([1, 1])
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assert torch.allclose(actual, desired)
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# Labels initializers
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def test_literal_labels_init():
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l = pt.initializers.LiteralLabelsInitializer([0, 0, 1, 2])
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with pytest.warns(UserWarning):
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labels = l.generate([])
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assert torch.allclose(labels, torch.LongTensor([0, 0, 1, 2]))
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def test_labels_init_from_list():
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l = pt.initializers.LabelsInitializer()
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components = l.generate(distribution=[1, 1, 1])
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assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
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def test_labels_init_from_tuple_legal():
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l = pt.initializers.LabelsInitializer()
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components = l.generate(distribution=(3, 1))
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assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
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def test_labels_init_from_tuple_illegal():
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l = pt.initializers.LabelsInitializer()
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with pytest.raises(AssertionError):
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_ = l.generate(distribution=(1, 1, 1))
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def test_data_aware_labels_init():
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data, targets = [0, 1, 2, 3], [0, 0, 1, 1]
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ds = pt.datasets.NumpyDataset(data, targets)
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l = pt.initializers.DataAwareLabelsInitializer(ds)
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labels = l.generate([])
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assert torch.allclose(labels, torch.LongTensor(targets))
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# Reasonings initializers
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def test_literal_reasonings_init():
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r = pt.initializers.LiteralReasoningsInitializer([0, 0, 1, 2])
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with pytest.warns(UserWarning):
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reasonings = r.generate([])
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assert torch.allclose(reasonings, torch.Tensor([0, 0, 1, 2]))
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def test_random_reasonings_init():
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r = pt.initializers.RandomReasoningsInitializer(0.2, 0.8)
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reasonings = r.generate(distribution=[0, 1])
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assert torch.numel(reasonings) == 1 * 2 * 2
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assert reasonings.min() >= 0.2
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assert reasonings.max() <= 0.8
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def test_zeros_reasonings_init():
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r = pt.initializers.ZerosReasoningsInitializer()
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reasonings = r.generate(distribution=[0, 1])
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assert torch.allclose(reasonings, torch.zeros(1, 2, 2))
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def test_ones_reasonings_init():
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r = pt.initializers.ZerosReasoningsInitializer()
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reasonings = r.generate(distribution=[1, 2, 3])
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assert torch.allclose(reasonings, torch.zeros(6, 3, 2))
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def test_pure_positive_reasonings_init_one_per_class():
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r = pt.initializers.PurePositiveReasoningsInitializer(
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components_first=False)
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reasonings = r.generate(distribution=(4, 1))
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assert torch.allclose(reasonings[0], torch.eye(4))
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def test_pure_positive_reasonings_init_unrepresented_classes():
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r = pt.initializers.PurePositiveReasoningsInitializer()
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reasonings = r.generate(distribution=[9, 0, 0, 0])
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assert reasonings.shape[0] == 9
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assert reasonings.shape[1] == 4
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assert reasonings.shape[2] == 2
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def test_random_reasonings_init_channels_not_first():
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r = pt.initializers.RandomReasoningsInitializer(components_first=False)
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reasonings = r.generate(distribution=[0, 0, 0, 1])
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assert reasonings.shape[0] == 2
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assert reasonings.shape[1] == 4
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assert reasonings.shape[2] == 1
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# Transform initializers
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def test_eye_transform_init_square():
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t = pt.initializers.EyeTransformInitializer()
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I = t.generate(3, 3)
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assert torch.allclose(I, torch.eye(3))
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def test_eye_transform_init_narrow():
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t = pt.initializers.EyeTransformInitializer()
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actual = t.generate(3, 2)
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desired = torch.Tensor([[1, 0], [0, 1], [0, 0]])
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assert torch.allclose(actual, desired)
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def test_eye_transform_init_wide():
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t = pt.initializers.EyeTransformInitializer()
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actual = t.generate(2, 3)
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desired = torch.Tensor([[1, 0, 0], [0, 1, 0]])
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assert torch.allclose(actual, desired)
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# Transforms
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def test_linear_transform():
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l = pt.transforms.LinearTransform(2, 4)
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actual = l.weights
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desired = torch.Tensor([[1, 0, 0, 0], [0, 1, 0, 0]])
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assert torch.allclose(actual, desired)
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def test_linear_transform_zeros_init():
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l = pt.transforms.LinearTransform(
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in_dim=2,
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out_dim=4,
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initializer=pt.initializers.ZerosLinearTransformInitializer(),
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)
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actual = l.weights
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desired = torch.zeros(2, 4)
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assert torch.allclose(actual, desired)
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def test_linear_transform_out_dim_first():
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l = pt.transforms.LinearTransform(
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in_dim=2,
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out_dim=4,
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initializer=pt.initializers.OLTI(out_dim_first=True),
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)
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assert l.weights.shape[0] == 4
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assert l.weights.shape[1] == 2
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# Components
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def test_components_no_initializer():
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with pytest.raises(TypeError):
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_ = pt.components.Components(3, None)
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def test_components_no_num_components():
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with pytest.raises(TypeError):
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_ = pt.components.Components(initializer=pt.initializers.OCI(2))
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def test_components_none_num_components():
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with pytest.raises(TypeError):
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_ = pt.components.Components(None, initializer=pt.initializers.OCI(2))
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def test_components_no_args():
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with pytest.raises(TypeError):
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_ = pt.components.Components()
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def test_components_zeros_init():
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c = pt.components.Components(3, pt.initializers.ZCI(2))
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assert torch.allclose(c.components, torch.zeros(3, 2))
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def test_labeled_components_dict_init():
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c = pt.components.LabeledComponents({0: 3}, pt.initializers.OCI(2))
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assert torch.allclose(c.components, torch.ones(3, 2))
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assert torch.allclose(c.labels, torch.zeros(3, dtype=torch.long))
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def test_labeled_components_list_init():
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c = pt.components.LabeledComponents([3], pt.initializers.OCI(2))
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assert torch.allclose(c.components, torch.ones(3, 2))
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assert torch.allclose(c.labels, torch.zeros(3, dtype=torch.long))
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def test_labeled_components_tuple_init():
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c = pt.components.LabeledComponents({0: 1, 1: 2}, pt.initializers.OCI(2))
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assert torch.allclose(c.components, torch.ones(3, 2))
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assert torch.allclose(c.labels, torch.LongTensor([0, 1, 1]))
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# Labels
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def test_standalone_labels_dict_init():
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l = pt.components.Labels({0: 3})
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assert torch.allclose(l.labels, torch.zeros(3, dtype=torch.long))
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def test_standalone_labels_list_init():
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l = pt.components.Labels([3])
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assert torch.allclose(l.labels, torch.zeros(3, dtype=torch.long))
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def test_standalone_labels_tuple_init():
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l = pt.components.Labels({0: 1, 1: 2})
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assert torch.allclose(l.labels, torch.LongTensor([0, 1, 1]))
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# Losses
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def test_glvq_loss_int_labels():
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d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
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labels = torch.tensor([0, 1])
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targets = torch.ones(100)
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batch_loss = pt.losses.glvq_loss(distances=d,
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target_labels=targets,
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prototype_labels=labels)
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loss_value = torch.sum(batch_loss, dim=0)
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assert loss_value == -100
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def test_glvq_loss_one_hot_labels():
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d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
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labels = torch.tensor([[0, 1], [1, 0]])
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wl = torch.tensor([1, 0])
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targets = torch.stack([wl for _ in range(100)], dim=0)
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batch_loss = pt.losses.glvq_loss(distances=d,
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target_labels=targets,
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prototype_labels=labels)
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loss_value = torch.sum(batch_loss, dim=0)
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assert loss_value == -100
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def test_glvq_loss_one_hot_unequal():
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dlist = [torch.ones(100), torch.zeros(100), torch.zeros(100)]
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d = torch.stack(dlist, dim=1)
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labels = torch.tensor([[0, 1], [1, 0], [1, 0]])
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wl = torch.tensor([1, 0])
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targets = torch.stack([wl for _ in range(100)], dim=0)
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batch_loss = pt.losses.glvq_loss(distances=d,
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target_labels=targets,
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prototype_labels=labels)
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loss_value = torch.sum(batch_loss, dim=0)
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assert loss_value == -100
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# Activations
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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(pt.nn.ACTIVATIONS)
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def test_funcname_deserialization(self):
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for funcname in self.flist:
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f = pt.nn.get_activation(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, **kwargs):
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return x
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for f in [dummy, lambda x: x]:
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f = pt.nn.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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_ = pt.nn.get_activation(funcname)
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def test_identity(self):
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actual = pt.nn.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 = pt.nn.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 = pt.nn.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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# Competitions
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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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competition_layer = pt.competitions.WTAC()
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actual = competition_layer(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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competition_layer = pt.competitions.WTAC()
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actual = competition_layer(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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competition_layer = pt.competitions.WTAC()
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actual = competition_layer(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_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]])
|
|
labels = torch.tensor([0, 1, 2, 3])
|
|
competition_layer = pt.competitions.KNNC(k=1)
|
|
actual = competition_layer(d, labels)
|
|
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])
|
|
pooling_layer = pt.pooling.StratifiedMinPooling()
|
|
actual = pooling_layer(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]
|
|
pooling_layer = pt.pooling.StratifiedMinPooling()
|
|
actual = pooling_layer(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])
|
|
pooling_layer = pt.pooling.StratifiedMinPooling()
|
|
actual = pooling_layer(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])
|
|
pooling_layer = pt.pooling.StratifiedMaxPooling()
|
|
actual = pooling_layer(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)
|
|
pooling_layer = pt.pooling.StratifiedMaxPooling()
|
|
actual = pooling_layer(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])
|
|
pooling_layer = pt.pooling.StratifiedSumPooling()
|
|
actual = pooling_layer(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]
|
|
pooling_layer = pt.pooling.StratifiedSumPooling()
|
|
actual = pooling_layer(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])
|
|
pooling_layer = pt.pooling.StratifiedProdPooling()
|
|
actual = pooling_layer(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
|