class TestStd(unittest.TestCase):
def setUp(self):
self._metric = Metric('test')
self._metric.process = Mock()
self._metric.process.side_effect = [torch.zeros(torch.Size([])),
torch.FloatTensor([0.1, 0.2, 0.3]),
torch.FloatTensor([0.4, 0.5, 0.6]),
torch.FloatTensor([0.7, 0.8, 0.9]),
torch.ones(torch.Size([]))]
self._std = Std('test')
self._std.reset({})
self._target = 0.31622776601684
def test_train(self):
self._std.train()
for i in range(5):
self._std.process(self._metric.process())
result = self._std.process_final({})
self.assertAlmostEqual(self._target, result)
def test_validate(self):
self._std.eval()
for i in range(5):
self._std.process(self._metric.process())
result = self._std.process_final({})
self.assertAlmostEqual(self._target, result)
class TestStd(unittest.TestCase):
def setUp(self):
self._metric = Metric('test')
self._metric.process = Mock()
self._metric.process.side_effect = [torch.zeros(torch.Size([])),
torch.FloatTensor([0.1, 0.2, 0.3]),
torch.FloatTensor([0.4, 0.5, 0.6]),
torch.FloatTensor([0.7, 0.8, 0.9]),
torch.ones(torch.Size([]))]
self._std = Std('test')
self._std.reset({})
self._target = 0.31622776601684
def test_train(self):
self.setUp()
self._std.train()
for i in range(5):
self._std.process(self._metric.process())
result = self._std.process_final({})
self.assertAlmostEqual(self._target, result, places=5)
def test_validate(self):
self.setUp()
self._std.eval()
for i in range(5):
self._std.process(self._metric.process())
result = self._std.process_final({})
self.assertAlmostEqual(self._target, result, places=5)
def test_precision_error(self):
self.setUp()
self._std.train()
val = torch.tensor([0.55])
for i in range(2):
self._std.process(val)
result = self._std.process_final({})
self.assertEqual(0, result)
def setUpMoreDims(self):
self._metric = Metric('test')
self._metric.process = Mock()
self._metric.process.side_effect = [torch.zeros(torch.Size([])),
torch.FloatTensor([[0.1, 0.2, 0.3], [1.1, 1.2, 1.3]]),
torch.FloatTensor([[0.4, 0.5, 0.6], [1.4, 1.5, 1.6]]),
torch.FloatTensor([[0.7, 0.8, 0.9], [1.7, 1.8, 1.9]]),
torch.ones(torch.Size([]))]
self._std = Std('test')
self._std.reset({})
self._target = 0.57662804083742
def test_more_dims(self):
self.setUpMoreDims()
for i in range(5):
self._std.process(self._metric.process())
result = self._std.process_final({})
self.assertAlmostEqual(self._target, result, places=5)