class TestXcamWithMultipleIOModel():
@pytest.mark.parametrize("scores,expected_error", [
(None, ValueError),
([None], ValueError),
(CategoricalScore(0), ValueError),
([CategoricalScore(0)], ValueError),
([None, None], ValueError),
([CategoricalScore(0), None], ValueError),
([None, BinaryScore(0)], ValueError),
([CategoricalScore(0), BinaryScore(0)], NO_ERROR),
([score_with_tuple, score_with_tuple], NO_ERROR),
([score_with_list, score_with_list], NO_ERROR),
])
@pytest.mark.usefixtures("xcam", "saliency", "mixed_precision")
def test__call__if_score_is_(self, scores, expected_error, multiple_io_model):
cam = Xcam(multiple_io_model)
with assert_raises(expected_error):
result = cam(scores, [dummy_sample((1, 8, 8, 3)), dummy_sample((1, 10, 10, 3))])
assert result[0].shape == (1, 8, 8)
assert result[1].shape == (1, 10, 10)
@pytest.mark.parametrize("seed_input,expected_error", [
(None, ValueError),
(dummy_sample((1, 8, 8, 3)), ValueError),
([dummy_sample((1, 8, 8, 3))], ValueError),
([dummy_sample((1, 8, 8, 3)), dummy_sample((1, 10, 10, 3))], NO_ERROR),
])
@pytest.mark.usefixtures("xcam", "saliency", "mixed_precision")
def test__call__if_seed_input_is_(self, seed_input, expected_error, multiple_io_model):
cam = Xcam(multiple_io_model)
with assert_raises(expected_error):
result = cam([CategoricalScore(0), BinaryScore(0)], seed_input)
assert result[0].shape == (1, 8, 8)
assert result[1].shape == (1, 10, 10)
@pytest.mark.parametrize("expand_cam", [False, True])
@pytest.mark.usefixtures("xcam", "mixed_precision")
def test__call__with_expand_cam(self, expand_cam, multiple_io_model):
cam = Xcam(multiple_io_model)
result = cam([CategoricalScore(0), BinaryScore(0)],
[dummy_sample(
(1, 8, 8, 3)), dummy_sample((1, 10, 10, 3))],
expand_cam=expand_cam)
if expand_cam:
assert result[0].shape == (1, 8, 8)
assert result[1].shape == (1, 10, 10)
else:
assert result.shape == (1, 8, 8)
def test__call__with_expand_cam(self, expand_cam, multiple_outputs_model):
cam = Xcam(multiple_outputs_model)
result = cam([CategoricalScore(0), BinaryScore(0)], [dummy_sample((1, 8, 8, 3))],
expand_cam=expand_cam)
if expand_cam:
assert result[0].shape == (1, 8, 8)
else:
assert result.shape == (1, 6, 6)
def _test_for_multiple_io(self, model):
saliency = Saliency(model)
result = saliency(
[CategoricalScore(0), BinaryScore(0)],
[dummy_sample((1, 8, 8, 3)),
dummy_sample((1, 10, 10, 3))])
assert len(result) == 2
assert result[0].shape == (1, 8, 8)
assert result[1].shape == (1, 10, 10)
def test__call__if_seed_input_is_(self, seed_input, expected, expected_error,
multiple_outputs_model):
cam = Xcam(multiple_outputs_model)
with assert_raises(expected_error):
result = cam([CategoricalScore(0), BinaryScore(0)], seed_input)
if type(expected) is list:
assert type(result) is list
expected = expected[0]
result = result[0]
assert result.shape == expected
def test__call__(self, target_values, output, expected, expected_error):
output = tf.constant(output, tf.float32)
score = BinaryScore(target_values)
with assert_raises(expected_error):
score_value = score(output)
assert tf.math.reduce_all(score_value == expected)
def test__init__(self, target_values, expected, expected_error):
with assert_raises(expected_error):
score = BinaryScore(target_values)
assert score.target_values == expected
def test__call__if_seed_input_is_(self, seed_input, expected_error, multiple_io_model):
cam = Xcam(multiple_io_model)
with assert_raises(expected_error):
result = cam([CategoricalScore(0), BinaryScore(0)], seed_input)
assert result[0].shape == (1, 8, 8)
assert result[1].shape == (1, 10, 10)