def test_value(self, input_data, expected_value):
percentile = None
if len(input_data) == 3:
[seg_1, seg_2, percentile] = input_data
else:
[seg_1, seg_2] = input_data
ct = 0
seg_1 = torch.tensor(seg_1)
seg_2 = torch.tensor(seg_2)
for metric in ["euclidean", "chessboard", "taxicab"]:
for directed in [True, False]:
hd_metric = HausdorffDistanceMetric(include_background=False,
distance_metric=metric,
percentile=percentile,
directed=directed)
# shape of seg_1, seg_2 are: HWD, converts to BNHWD
batch, n_class = 2, 3
batch_seg_1 = seg_1.unsqueeze(0).unsqueeze(0).repeat(
[batch, n_class, 1, 1, 1])
batch_seg_2 = seg_2.unsqueeze(0).unsqueeze(0).repeat(
[batch, n_class, 1, 1, 1])
hd_metric(batch_seg_1, batch_seg_2)
result = hd_metric.aggregate()
expected_value_curr = expected_value[ct]
np.testing.assert_allclose(expected_value_curr,
result,
rtol=1e-7)
ct += 1
def test_nans(self, input_data):
[seg_1, seg_2] = input_data
seg_1 = torch.tensor(seg_1)
seg_2 = torch.tensor(seg_2)
hd_metric = HausdorffDistanceMetric(include_background=False, get_not_nans=True)
batch_seg_1 = seg_1.unsqueeze(0).unsqueeze(0)
batch_seg_2 = seg_2.unsqueeze(0).unsqueeze(0)
hd_metric(batch_seg_1, batch_seg_2)
result, not_nans = hd_metric.aggregate()
np.testing.assert_allclose(0, result, rtol=1e-7)
np.testing.assert_allclose(0, not_nans, rtol=1e-7)