def test_functional_threshold(self):
threshold = -0.5
actual = bin_acc(self.predictions, self.targets, threshold=threshold)
label_predictions = (self.predictions > threshold).float()
self.assertEqual((), actual.shape)
np.testing.assert_almost_equal(
(label_predictions == self.targets).float().mean() * 100, actual)
def test_functional_none(self):
actual = bin_acc(self.predictions, self.targets, reduction='none')
self.assertEqual(self.targets.shape, actual.shape)
self.assertTrue(
torch.all(
100. *
(self.label_predictions == self.targets).float() == actual))
def test_functional_on_gpu(self):
actual = bin_acc(self.predictions.cuda(), self.targets.cuda())
self.assertEqual((), actual.shape)
np.testing.assert_almost_equal(self.binary_accuracy, actual.cpu())
def test_functional_sum(self):
actual = bin_acc(self.predictions, self.targets, reduction='sum')
self.assertEqual((), actual.shape)
np.testing.assert_almost_equal(
(self.label_predictions == self.targets).float().sum() * 100,
actual)
def test_functional(self):
actual = bin_acc(self.predictions, self.targets)
self.assertEqual((), actual.shape)
np.testing.assert_almost_equal(self.binary_accuracy, actual)
def test_functional_none(self):
actual = bin_acc(self.predictions, self.targets, reduction='none')
self.assertEqual(self.targets.shape, actual.shape)
self.assertTrue(torch.all(self.binary_accuracy_none == actual))
def test_functional_sum(self):
actual = bin_acc(self.predictions, self.targets, reduction='sum')
self.assertEqual((), actual.shape)
np.testing.assert_almost_equal(self.binary_accuracy_none.sum(), actual)