def test_reduction(self):
sur_metric = SurfaceDistance(include_background=True,
reduction="mean_channel")
def _val_func(engine, batch):
pass
engine = Engine(_val_func)
sur_metric.attach(engine, "surface_distance")
y_pred, y = TEST_SAMPLE_1
sur_metric.update([y_pred, y])
y_pred, y = TEST_SAMPLE_2
sur_metric.update([y_pred, y])
torch.testing.assert_allclose(sur_metric.compute().float(),
torch.tensor([4.1713, 0.0000]))
def test_compute(self):
sur_metric = SurfaceDistance(include_background=True)
def _val_func(engine, batch):
pass
engine = Engine(_val_func)
sur_metric.attach(engine, "surface_distance")
y_pred, y = TEST_SAMPLE_1
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), 4.17133, places=4)
y_pred, y = TEST_SAMPLE_2
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), 2.08566, places=4)
y_pred, y = TEST_SAMPLE_3
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), float("inf"))
y_pred, y = TEST_SAMPLE_4
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), float("inf"))
def test_shape_mismatch(self):
sur_metric = SurfaceDistance(include_background=True)
with self.assertRaises((AssertionError, ValueError)):
y_pred = TEST_SAMPLE_1[0]
y = torch.ones((1, 1, 10, 10, 10))
sur_metric.update([y_pred, y])
def test_compute(self):
sur_metric = SurfaceDistance(include_background=True)
y_pred, y = TEST_SAMPLE_1
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), 4.17133, places=4)
y_pred, y = TEST_SAMPLE_2
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), 2.08566, places=4)
y_pred, y = TEST_SAMPLE_3
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric.compute(), float("inf"))
self.assertAlmostEqual(sur_metric._num_examples, 3)
y_pred, y = TEST_SAMPLE_4
sur_metric.update([y_pred, y])
self.assertAlmostEqual(sur_metric._num_examples, 3)