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[BUGFIX] Update unit tests

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This commit is contained in:
Jensun Ravichandran 2021-06-04 22:29:30 +02:00
parent 2272c55092
commit 4f1c879528
1 changed files with 27 additions and 19 deletions
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46
tests/test_functions.py
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@ -5,7 +5,7 @@ import unittest
import numpy as np import numpy as np
import torch import torch
from prototorch.functions import (activations, competitions, distances, from prototorch.functions import (activations, competitions, distances,
initializers, losses) initializers, losses, pooling)
class TestActivations(unittest.TestCase): class TestActivations(unittest.TestCase):
@ -104,10 +104,28 @@ class TestCompetitions(unittest.TestCase):
decimal=5) decimal=5)
self.assertIsNone(mismatch) self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=1)
desired = torch.tensor([2, 0])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self):
pass
class TestPooling(unittest.TestCase):
def setUp(self):
pass
def test_stratified_min(self): def test_stratified_min(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]]) 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.tensor([0, 0, 1, 2])
actual = competitions.stratified_min(d, labels) actual = pooling.stratified_min_pooling(d, labels)
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]]) desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -118,7 +136,7 @@ class TestCompetitions(unittest.TestCase):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]]) 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.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels] labels = torch.eye(3)[labels]
actual = competitions.stratified_min(d, labels) actual = pooling.stratified_min_pooling(d, labels)
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]]) desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -128,7 +146,7 @@ class TestCompetitions(unittest.TestCase):
def test_stratified_min_trivial(self): def test_stratified_min_trivial(self):
d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]]) d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
labels = torch.tensor([0, 1, 2]) labels = torch.tensor([0, 1, 2])
actual = competitions.stratified_min(d, labels) actual = pooling.stratified_min_pooling(d, labels)
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]]) desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -138,7 +156,7 @@ class TestCompetitions(unittest.TestCase):
def test_stratified_max(self): 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]]) 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]) labels = torch.tensor([0, 0, 3, 2, 0])
actual = competitions.stratified_max(d, labels) actual = pooling.stratified_max_pooling(d, labels)
desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]]) desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -149,7 +167,7 @@ class TestCompetitions(unittest.TestCase):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]]) 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.tensor([0, 0, 2, 1, 0])
labels = torch.nn.functional.one_hot(labels, num_classes=3) labels = torch.nn.functional.one_hot(labels, num_classes=3)
actual = competitions.stratified_max(d, labels) actual = pooling.stratified_max_pooling(d, labels)
desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]]) desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -159,7 +177,7 @@ class TestCompetitions(unittest.TestCase):
def test_stratified_sum(self): def test_stratified_sum(self):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]]) d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.LongTensor([0, 0, 1, 2]) labels = torch.LongTensor([0, 0, 1, 2])
actual = competitions.stratified_sum(d, labels) actual = pooling.stratified_sum_pooling(d, labels)
desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]]) desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -170,7 +188,7 @@ class TestCompetitions(unittest.TestCase):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]]) 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.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels] labels = torch.eye(3)[labels]
actual = competitions.stratified_sum(d, labels) actual = pooling.stratified_sum_pooling(d, labels)
desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]]) desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -180,23 +198,13 @@ class TestCompetitions(unittest.TestCase):
def test_stratified_prod(self): 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]]) 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]) labels = torch.tensor([0, 0, 3, 2, 0])
actual = competitions.stratified_prod(d, labels) actual = pooling.stratified_prod_pooling(d, labels)
desired = torch.tensor([[0.0, 3.0, 2.0], [504.0, 1.0, 0.0]]) desired = torch.tensor([[0.0, 3.0, 2.0], [504.0, 1.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
decimal=5) decimal=5)
self.assertIsNone(mismatch) self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=1)
desired = torch.tensor([2, 0])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self): def tearDown(self):
pass pass