class TestRunningMean(unittest.TestCase):
def setUp(self):
self._metric = Metric('test')
self._mean = RunningMean('test')
self._cache = [
torch.Tensor([1.0]),
torch.Tensor([1.5]),
torch.Tensor([2.0])
]
self._target = 1.5
def test_train(self):
result = self._mean._process_train(torch.FloatTensor([1.0, 1.5, 2.0]))
self.assertAlmostEqual(self._target, result, 3, 0.002)
def test_step(self):
result = self._mean._step(self._cache)
self.assertEqual(self._target, result)
def test_dims(self):
mean = RunningMean('test', dim=0)
cache = [
mean._process_train(torch.Tensor([[1., 2.], [3., 4.]])),
mean._process_train(torch.Tensor([[4., 3.], [2., 1.]])),
mean._process_train(torch.Tensor([[1., 1.], [1., 1.]]))
]
res = mean._step(cache)
self.assertTrue(len(res) == 2)
for m in res:
self.assertTrue(abs(m - 2.0) < 0.0001)
def test_dims(self):
mean = RunningMean('test', dim=0)
cache = [
mean._process_train(torch.Tensor([[1., 2.], [3., 4.]])),
mean._process_train(torch.Tensor([[4., 3.], [2., 1.]])),
mean._process_train(torch.Tensor([[1., 1.], [1., 1.]]))
]
res = mean._step(cache)
self.assertTrue(len(res) == 2)
for m in res:
self.assertTrue(abs(m - 2.0) < 0.0001)
class TestRunningMean(unittest.TestCase):
def setUp(self):
self._metric = Metric('test')
self._mean = RunningMean('test')
self._cache = [1.0, 1.5, 2.0]
self._target = 1.5
def test_train(self):
result = self._mean._process_train(torch.FloatTensor([1.0, 1.5, 2.0]))
self.assertAlmostEqual(self._target, result, 3, 0.002)
def test_step(self):
result = self._mean._step(self._cache)
self.assertEqual(self._target, result)