def test_compare_random_converges(target, source):
"""Determine that the scores of random patches are in correct range.
"""
matcher1 = FeatureMatcher(target, source)
matcher1.compare_features_matrix(split=2)
matcher2 = FeatureMatcher(target, source)
for _ in range(500):
matcher2.compare_features_random(split=2)
missing = (
(matcher1.repro_target.indices != matcher2.repro_target.indices).sum()
+ (matcher1.repro_sources.indices != matcher2.repro_sources.indices).sum()
)
if missing == 0:
break
assert (matcher1.repro_target.indices != matcher2.repro_target.indices).sum() <= 2
assert pytest.approx(0.0, abs=1e-6) == torch.dist(
matcher1.repro_target.scores, matcher2.repro_target.scores
)
assert matcher2.repro_sources.indices.min() != -1
assert (matcher1.repro_sources.indices != matcher2.repro_sources.indices).sum() <= 2
assert pytest.approx(0.0, abs=1e-6) == torch.dist(
matcher1.repro_sources.scores, matcher2.repro_sources.scores
)
def test_scores_improve(content, style):
"""Scores must be one if inputs only vary on one dimension.
"""
matcher = FeatureMatcher(content, style)
matcher.compare_features_identity()
before = matcher.repro_target.scores.sum()
matcher.compare_features_random(times=1)
after = matcher.repro_target.scores.sum()
event("equal? %i" % int(after == before))
assert after >= before
def test_scores_range_random(target, source):
"""Determine that the scores of random patches are in correct range.
"""
matcher = FeatureMatcher(target, source)
matcher.compare_features_random(split=1)
assert matcher.repro_sources.indices.min() != -1
assert matcher.repro_target.scores.min() >= 0.0
assert matcher.repro_target.scores.max() <= 1.0
assert matcher.repro_sources.scores.max() >= 0.0
assert matcher.repro_sources.indices.max() != -1
def test_scores_target_bias_random(array):
matcher = FeatureMatcher(torch.cat([array, array], dim=2), array)
matcher.repro_sources.biases[:, :, 12:] = 1.0
matcher.repro_sources.scores.fill_(-1.0)
for _ in range(10):
matcher.compare_features_random(split=2)
assert (matcher.repro_sources.indices[:, 0] >= 12).all()
matcher.repro_sources.biases[:, :, 12:] = 0.0
matcher.repro_sources.biases[:, :, :12] = 1.0
matcher.repro_sources.scores.fill_(-1.0)
for _ in range(10):
matcher.compare_features_random(split=2)
assert (matcher.repro_sources.indices[:, 0] < 12).all()
def test_scores_source_bias_random(array):
matcher = FeatureMatcher(array, torch.cat([array, array], dim=2))
matcher.repro_target.biases[:, :, 8:] = 1.0
matcher.repro_target.scores.fill_(-1.0)
for _ in range(10):
matcher.compare_features_random(split=2)
assert (matcher.repro_target.indices[:, 0] >= 8).all()
matcher.repro_target.biases[:, :, 8:] = 0.0
matcher.repro_target.biases[:, :, :8] = 1.0
matcher.repro_target.scores.fill_(-1.0)
for _ in range(10):
matcher.compare_features_random(split=2)
assert (matcher.repro_target.indices[:, 0] < 8).all()
def test_indices_symmetry_random(content, style):
"""The indices of the symmerical operation must be the same.
"""
matcher1 = FeatureMatcher(content, style)
matcher2 = FeatureMatcher(style, content)
for _ in range(25):
matcher1.compare_features_random()
matcher2.compare_features_random()
missing = sum(
[
(matcher1.repro_target.indices != matcher2.repro_sources.indices).sum(),
(matcher1.repro_sources.indices != matcher2.repro_target.indices).sum(),
]
)
if missing == 0:
break
assert (matcher1.repro_target.indices != matcher2.repro_sources.indices).sum() <= 2
assert (matcher1.repro_sources.indices != matcher2.repro_target.indices).sum() <= 2