julius/prototorch
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[BUGFIX] Update unit tests

Browse Source
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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

46
tests/test_functions.py
View File

@ -5,7 +5,7 @@ import unittest
import numpy as np
import torch
from prototorch.functions import (activations, competitions, distances,
initializers, losses)
initializers, losses, pooling)
class TestActivations(unittest.TestCase):
@ -104,10 +104,28 @@ class TestCompetitions(unittest.TestCase):
decimal=5)
self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=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):
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
actual = competitions.stratified_min(d, labels)
actual = 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,
@ -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]])
labels = torch.tensor([0, 0, 1, 2])
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]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -128,7 +146,7 @@ class TestCompetitions(unittest.TestCase):
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 = 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]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -138,7 +156,7 @@ class TestCompetitions(unittest.TestCase):
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 = 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]])
mismatch = np.testing.assert_array_almost_equal(actual,
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]])
labels = torch.tensor([0, 0, 2, 1, 0])
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]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -159,7 +177,7 @@ class TestCompetitions(unittest.TestCase):
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 = 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]])
mismatch = np.testing.assert_array_almost_equal(actual,
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]])
labels = torch.tensor([0, 0, 1, 2])
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]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -180,23 +198,13 @@ class TestCompetitions(unittest.TestCase):
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 = 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]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=1)
desired = torch.tensor([2, 0])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self):
pass