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This commit is contained in:
Alexander Engelsberger
2021-04-23 17:24:53 +02:00
parent e1d56595c1
commit 7c30ffe2c7
28 changed files with 393 additions and 321 deletions
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28
tests/test_datasets.py
View File

@@ -12,26 +12,26 @@ from prototorch.datasets import abstract, tecator
class TestAbstract(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.Dataset('./artifacts')[0]
abstract.Dataset("./artifacts")[0]
def test_len(self):
with self.assertRaises(NotImplementedError):
len(abstract.Dataset('./artifacts'))
len(abstract.Dataset("./artifacts"))
class TestProtoDataset(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset('./artifacts')[0]
abstract.ProtoDataset("./artifacts")[0]
def test_download(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset('./artifacts').download()
abstract.ProtoDataset("./artifacts").download()
class TestTecator(unittest.TestCase):
def setUp(self):
self.artifacts_dir = './artifacts/Tecator'
self.artifacts_dir = "./artifacts/Tecator"
self._remove_artifacts()
def _remove_artifacts(self):
@@ -39,23 +39,23 @@ class TestTecator(unittest.TestCase):
shutil.rmtree(self.artifacts_dir)
def test_download_false(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
self._remove_artifacts()
with self.assertRaises(RuntimeError):
_ = tecator.Tecator(rootdir, download=False)
def test_download_caching(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
_ = tecator.Tecator(rootdir, download=True, verbose=False)
_ = tecator.Tecator(rootdir, download=False, verbose=False)
def test_repr(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
train = tecator.Tecator(rootdir, download=True, verbose=True)
self.assertTrue('Split: Train' in train.__repr__())
self.assertTrue("Split: Train" in train.__repr__())
def test_download_train(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
train = tecator.Tecator(root=rootdir,
train=True,
download=True,
@@ -67,7 +67,7 @@ class TestTecator(unittest.TestCase):
self.assertEqual(x_train.shape[1], 100)
def test_download_test(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
x_test, y_test = test.data, test.targets
self.assertEqual(x_test.shape[0], 71)
@@ -75,19 +75,19 @@ class TestTecator(unittest.TestCase):
self.assertEqual(x_test.shape[1], 100)
def test_class_to_idx(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
_ = test.class_to_idx
def test_getitem(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
x, y = test[0]
self.assertEqual(x.shape[0], 100)
self.assertIsInstance(y, int)
def test_loadable_with_dataloader(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
_ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)

78
tests/test_functions.py
View File

@@ -11,7 +11,7 @@ from prototorch.functions import (activations, competitions, distances,
class TestActivations(unittest.TestCase):
def setUp(self):
self.flist = ['identity', 'sigmoid_beta', 'swish_beta']
self.flist = ["identity", "sigmoid_beta", "swish_beta"]
self.x = torch.randn(1024, 1)
def test_registry(self):
@@ -39,7 +39,7 @@ class TestActivations(unittest.TestCase):
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ['blubb', 'foobar']:
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = activations.get_activation(funcname)
@@ -76,7 +76,7 @@ class TestCompetitions(unittest.TestCase):
pass
def test_wtac(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
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.wtac(d, labels)
desired = torch.tensor([2, 0])
@@ -86,7 +86,7 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_wtac_unequal_dist(self):
d = torch.tensor([[2., 3., 4.], [2., 3., 1.]])
d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
labels = torch.tensor([0, 1, 1])
actual = competitions.wtac(d, labels)
desired = torch.tensor([0, 1])
@@ -96,7 +96,7 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_wtac_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3., 2.01]])
d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
actual = competitions.wtac(d, labels)
desired = torch.tensor([[0, 1], [1, 0]])
@@ -106,38 +106,38 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_stratified_min(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 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])
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
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., 2., 3.], [9., 8., 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.eye(3)[labels]
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
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_simple(self):
d = torch.tensor([[0., 2., 3.], [8., 0, 1]])
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)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
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_knnc_k1(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
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=torch.tensor([1]))
desired = torch.tensor([2, 0])
@@ -194,12 +194,12 @@ class TestDistances(unittest.TestCase):
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(
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
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@@ -254,14 +254,14 @@ class TestDistances(unittest.TestCase):
self.assertIsNone(mismatch)
def test_lpnorm_pinf(self):
actual = distances.lpnorm_distance(self.x, self.y, p=float('inf'))
actual = 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'),
p=float("inf"),
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
@@ -275,12 +275,12 @@ class TestDistances(unittest.TestCase):
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(
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
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@@ -293,12 +293,12 @@ class TestDistances(unittest.TestCase):
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(
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
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@@ -311,8 +311,12 @@ class TestDistances(unittest.TestCase):
class TestInitializers(unittest.TestCase):
def setUp(self):
self.flist = [
'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
'stratified_random'
"zeros",
"ones",
"rand",
"randn",
"stratified_mean",
"stratified_random",
]
self.x = torch.tensor(
[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
@@ -340,7 +344,7 @@ class TestInitializers(unittest.TestCase):
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ['blubb', 'foobar']:
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = initializers.get_initializer(funcname)
@@ -383,7 +387,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal1(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@@ -393,7 +397,7 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@@ -402,8 +406,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal2(self):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
[1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@@ -413,8 +417,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
[0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@@ -423,8 +427,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@@ -434,8 +438,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@@ -444,8 +448,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
desired1 = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
@@ -460,8 +464,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
desired1 = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired1 = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,

119
tests/test_modules.py
View File

@@ -29,10 +29,12 @@ class TestPrototypes(unittest.TestCase):
_ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
def test_prototypes1d_init_without_pdist(self):
p1 = prototypes.Prototypes1D(input_dim=6,
nclasses=2,
prototypes_per_class=4,
prototype_initializer='ones')
p1 = prototypes.Prototypes1D(
input_dim=6,
nclasses=2,
prototypes_per_class=4,
prototype_initializer="ones",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.ones(8, 6)
@@ -45,7 +47,7 @@ class TestPrototypes(unittest.TestCase):
pdist = [2, 2]
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@@ -60,14 +62,15 @@ class TestPrototypes(unittest.TestCase):
input_dim=3,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None)
prototype_initializer="stratified_mean",
data=None,
)
def test_prototypes1d_init_torch_pdist(self):
pdist = torch.tensor([2, 2])
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@@ -77,24 +80,30 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_init_without_inputdim_with_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1], [0]], [1, 0]])
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
)
def test_prototypes1d_init_one_hot_without_data(self):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None,
one_hot_labels=True)
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=None,
one_hot_labels=True,
)
def test_prototypes1d_init_one_hot_labels_false(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `False`
@@ -105,9 +114,10 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0, 1], [1, 0]]),
one_hot_labels=False)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
one_hot_labels=False,
)
def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
@@ -118,9 +128,10 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [0, 1]),
one_hot_labels=True)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [0, 1]),
one_hot_labels=True,
)
def test_prototypes1d_init_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
@@ -132,25 +143,27 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0], [1]]),
one_hot_labels=True)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0], [1]]),
one_hot_labels=True,
)
def test_prototypes1d_init_with_int_dtype(self):
with self.assertRaises(RuntimeError):
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
dtype=torch.int32)
dtype=torch.int32,
)
def test_prototypes1d_inputndim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=1,
prototypes_per_class=1,
data=[[1.], [1]])
data=[[1.0], [1]])
def test_prototypes1d_inputdim_with_data(self):
with self.assertRaises(ValueError):
@@ -158,8 +171,9 @@ class TestPrototypes(unittest.TestCase):
input_dim=2,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_nclasses_with_data(self):
"""Test ValueError raise if provided `nclasses` is not the same
@@ -170,13 +184,14 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=1,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [2.]], [1, 2]])
prototype_initializer="stratified_mean",
data=[[[1.0], [2.0]], [1, 2]],
)
def test_prototypes1d_init_with_ppc(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototypes_per_class=2,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@@ -186,9 +201,11 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_init_with_pdist(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototype_distribution=[6, 9],
prototype_initializer='zeros')
p1 = prototypes.Prototypes1D(
data=[self.x, self.y],
prototype_distribution=[6, 9],
prototype_initializer="zeros",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(15, 3)
@@ -201,10 +218,12 @@ class TestPrototypes(unittest.TestCase):
def my_initializer(*args, **kwargs):
return torch.full((2, 99), 99.0), torch.tensor([0, 1])
p1 = prototypes.Prototypes1D(input_dim=99,
nclasses=2,
prototypes_per_class=1,
prototype_initializer=my_initializer)
p1 = prototypes.Prototypes1D(
input_dim=99,
nclasses=2,
prototypes_per_class=1,
prototype_initializer=my_initializer,
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = 99 * torch.ones(2, 99)
@@ -231,7 +250,7 @@ class TestPrototypes(unittest.TestCase):
def test_prototypes1d_validate_extra_repr_not_empty(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
rep = p1.extra_repr()
self.assertNotEqual(rep, '')
self.assertNotEqual(rep, "")
def tearDown(self):
del self.x, self.y, self.gen
@@ -243,11 +262,11 @@ class TestLosses(unittest.TestCase):
pass
def test_glvqloss_init(self):
_ = losses.GLVQLoss(0, 'swish_beta', beta=20)
_ = losses.GLVQLoss(0, "swish_beta", beta=20)
def test_glvqloss_forward_1ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
squashing="sigmoid_beta",
beta=100)
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([0, 1])
@@ -259,7 +278,7 @@ class TestLosses(unittest.TestCase):
def test_glvqloss_forward_2ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
squashing="sigmoid_beta",
beta=100)
d = torch.stack([
torch.ones(100),