def test_indices_linear_range(content, style):
"""Determine that random indices are in range.
"""
pm = patchmatch.PatchMatcher(content, style, indices='linear')
assert pm.indices[:, 0, :, :].min() >= 0
assert pm.indices[:, 0, :, :].max() < style.shape[2]
assert pm.indices[:, 1, :, :].min() >= 0
assert pm.indices[:, 1, :, :].max() < style.shape[3]
def test_scores_improve(content, style):
"""Scores must be one if inputs only vary on one dimension.
"""
pm = patchmatch.PatchMatcher(content, style)
before = pm.scores.sum()
pm.search_patches_random(times=1)
after = pm.scores.sum()
event("equal? %i" % int(after == before))
assert after >= before
def test_indices_linear(array):
"""Indices of the indentity transformation should be linear.
"""
pm = patchmatch.PatchMatcher(array, array, indices='linear')
assert (pm.indices[:, 0, :, :] == torch.arange(start=0,
end=array.shape[2]).view(
1, -1, 1)).all()
assert (pm.indices[:, 1, :, :] == torch.arange(start=0,
end=array.shape[3]).view(
1, 1, -1)).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.
"""
pm = patchmatch.PatchMatcher(array, array, indices='zero')
pm.search_patches_propagate(steps=[1])
assert (pm.indices[:, :, 1, 0] == torch.tensor([1, 0],
dtype=torch.long)).all()
assert (pm.indices[:, :, 0, 1] == torch.tensor([0, 1],
dtype=torch.long)).all()
pm.search_patches_propagate(steps=[1])
assert (pm.indices[:, :, 1, 1] == torch.tensor([1, 1],
dtype=torch.long)).all()
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:]
pm = patchmatch.PatchMatcher(array, array, indices='zero')
pm.indices[:, 0, -1, -1] = y - 1
pm.indices[:, 1, -1, -1] = x - 1
pm.improve_patches(pm.indices)
pm.search_patches_propagate(steps=[1])
assert (pm.indices[:, :,
y - 2, x - 1] == torch.tensor([y - 2, x - 1],
dtype=torch.long)).all()
assert (pm.indices[:, :,
y - 1, x - 2] == torch.tensor([y - 1, x - 2],
dtype=torch.long)).all()
pm.search_patches_propagate(steps=[1])
assert (pm.indices[:, :,
y - 2, x - 2] == torch.tensor([y - 2, x - 2],
dtype=torch.long)).all()
def test_scores_one(content, style):
"""Scores must be one if inputs only vary on one dimension.
"""
content[:, 0, :, :], style[:, 0, :, :] = 0.0, 0.0
pm = patchmatch.PatchMatcher(content, style)
assert pytest.approx(1.0) == pm.scores.min()
def test_scores_zero(content, style):
"""Scores must be zero if inputs vary on different dimensions.
"""
content[:, :, 0], style[:, :, 1] = 0.0, 0.0
pm = patchmatch.PatchMatcher(content, style)
assert pytest.approx(0.0) == pm.scores.max()
def test_scores_identity(array):
"""The score of the identity operation with linear indices should be one.
"""
pm = patchmatch.PatchMatcher(array, array, indices='linear')
assert pytest.approx(1.0) == pm.scores.min()
def test_indices_random(content, style):
"""Determine that random indices are indeed random in larger grids.
"""
pm = patchmatch.PatchMatcher(content, style)
assert pm.indices.min() != pm.indices.max()
def test_scores_range(content, style):
"""Determine that the scores of random patches are in correct range.
"""
pm = patchmatch.PatchMatcher(content, style)
assert pm.scores.min() >= 0.0
assert pm.scores.max() <= 1.0