def test_propagate_up_left(array):
"""Propagating the identity transformation expects indices to propagate
one cell at a time, here up and towards the left.
"""
y, x = array.shape[-2:]
matcher = FeatureMatcher(array, array)
indices, scores = matcher.repro_target.indices, matcher.repro_target.scores
indices.zero_()
indices[:, 0, -1, -1] = y - 1
indices[:, 1, -1, -1] = x - 1
scores[:, 0, -1, -1] = 1.0
matcher.compare_features_nearby(radius=1, split=2)
assert (
indices[:, :, y - 2, x - 1] == torch.tensor([y - 2, x - 1], dtype=torch.long)
).all()
assert (
indices[:, :, y - 1, x - 2] == torch.tensor([y - 1, x - 2], dtype=torch.long)
).all()
matcher.compare_features_nearby(radius=1, split=2)
assert (
indices[:, :, y - 2, x - 2] == torch.tensor([y - 2, x - 2], dtype=torch.long)
).all()
def test_propagate_down_right(array):
"""Propagating the identity transformation expects indices to propagate
one cell at a time, this time down and towards the right.
"""
matcher = FeatureMatcher(array, array)
indices = matcher.repro_target.indices
indices.zero_()
matcher.compare_features_nearby(radius=1, split=2)
assert (indices[:, :, 1, 0] == torch.tensor([1, 0], dtype=torch.long)).all()
assert (indices[:, :, 0, 1] == torch.tensor([0, 1], dtype=torch.long)).all()
matcher.compare_features_nearby(radius=1, split=2)
assert (indices[:, :, 1, 1] == torch.tensor([1, 1], dtype=torch.long)).all()