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Add test cases

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This commit is contained in:
blackfly 2020-04-08 22:47:08 +02:00
parent b19cbcb76a
commit 21b0279839
2 changed files with 148 additions and 102 deletions
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236
tests/test_functions.py
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@ -6,7 +6,107 @@ import numpy as np
import torch
from prototorch.functions import (activations, competitions, distances,
initializers)
initializers, losses)
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(activations.ACTIVATIONS)
def test_funcname_deserialization(self):
for funcname in self.flist:
f = activations.get_activation(funcname)
iscallable = callable(f)
self.assertTrue(iscallable)
# def test_torch_script(self):
# for funcname in self.flist:
# f = activations.get_activation(funcname)
# self.assertIsInstance(f, torch.jit.ScriptFunction)
def test_callable_deserialization(self):
def dummy(x, **kwargs):
return x
for f in [dummy, lambda x: x]:
f = activations.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):
_ = activations.get_activation(funcname)
def test_identity(self):
actual = activations.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 = activations.sigmoid_beta(self.x, beta=torch.tensor(1))
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 = activations.swish_beta(self.x, beta=torch.tensor(1))
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
class TestCompetitions(unittest.TestCase):
def setUp(self):
pass
def test_wtac(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
labels = torch.tensor([0, 1, 2, 3])
actual = 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_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3., 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
actual = 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., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=torch.tensor([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 TestDistances(unittest.TestCase):
@ -167,103 +267,12 @@ class TestDistances(unittest.TestCase):
del self.x, self.y
class TestActivations(unittest.TestCase):
def setUp(self):
self.x = torch.randn(1024, 1)
def test_registry(self):
self.assertIsNotNone(activations.ACTIVATIONS)
def test_funcname_deserialization(self):
flist = ['identity', 'sigmoid_beta', 'swish_beta']
for funcname in flist:
f = activations.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 = activations.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):
_ = activations.get_activation(funcname)
def test_identity(self):
actual = activations.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 = activations.sigmoid_beta(self.x, beta=1)
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 = activations.swish_beta(self.x, beta=1)
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
class TestCompetitions(unittest.TestCase):
def setUp(self):
pass
def test_wtac(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
labels = torch.tensor([0, 1, 2, 3])
actual = 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_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3., 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
actual = 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., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
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 TestInitializers(unittest.TestCase):
def setUp(self):
self.flist = [
'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
'stratified_random'
]
self.x = torch.tensor(
[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
dtype=torch.float32)
@ -274,11 +283,7 @@ class TestInitializers(unittest.TestCase):
self.assertIsNotNone(initializers.INITIALIZERS)
def test_funcname_deserialization(self):
flist = [
'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
'stratified_random'
]
for funcname in flist:
for funcname in self.flist:
f = initializers.get_initializer(funcname)
iscallable = callable(f)
self.assertTrue(iscallable)
@ -385,3 +390,32 @@ class TestInitializers(unittest.TestCase):
def tearDown(self):
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 tearDown(self):
pass

14
tests/test_modules.py
View File

@ -123,7 +123,19 @@ class TestLosses(unittest.TestCase):
pass
def test_glvqloss_init(self):
_ = losses.GLVQLoss()
_ = losses.GLVQLoss(0, 'swish_beta', beta=20)
def test_glvqloss_forward(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
beta=100)
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([0, 1])
targets = torch.ones(100)
outputs = [d, labels]
loss = criterion(outputs, targets)
loss_value = loss.item()
self.assertAlmostEqual(loss_value, 0.0)
def tearDown(self):
pass