def test__call__if_score_is_(self, scores, expected_error, multiple_inputs_model):
cam = Xcam(multiple_inputs_model)
with assert_raises(expected_error):
result = cam(scores, [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_max_N_is_(self, max_N, expected_error, conv_model):
with assert_raises(expected_error):
cam = Scorecam(conv_model)
result = cam(CategoricalScore(0),
dummy_sample((2, 8, 8, 3)),
max_N=max_N)
assert result.shape == (2, 8, 8)
def test__call__(self, model, layer, expected_error):
assert model.outputs[0].shape.as_list() == [None, 2]
with assert_raises(expected_error):
instance = ActivationMaximization(model,
model_modifier=ExtractIntermediateLayer(layer))
assert instance.model != model
assert instance.model.outputs[0].shape.as_list() == [None, 6, 6, 6]
instance([CategoricalScore(0)])
def test__init__(self, degree, axes, expected_error):
with assert_raises(expected_error):
if axes is None:
instance = Rotate(degree=degree)
else:
instance = Rotate(axes=axes, degree=degree)
assert instance.axes == axes
assert instance.degree == float(degree)
def test__call__if_penultimate_layer_is(self, penultimate_layer, seek_penultimate_conv_layer,
expected_error, conv_model):
cam = Xcam(conv_model)
with assert_raises(expected_error):
result = cam(CategoricalScore(0),
dummy_sample((1, 8, 8, 3)),
penultimate_layer=penultimate_layer,
seek_penultimate_conv_layer=seek_penultimate_conv_layer)
assert result.shape == (1, 8, 8)
def test__call__if_seed_input_is_(self, seed_input, expected, expected_error, conv_model):
cam = Xcam(conv_model)
with assert_raises(expected_error):
result = cam(CategoricalScore(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__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, indices, output_shape, expected_error):
output = tf.constant(dummy_sample(output_shape), tf.float32)
score = CategoricalScore(indices)
with assert_raises(expected_error):
score_value = score(output)
assert score_value.shape == output_shape[0:1]
def test__init__(self, indices, expected, expected_error):
with assert_raises(expected_error):
score = CategoricalScore(indices)
assert score.indices == 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__(self, output, expected_shape, expected_error):
with assert_raises(expected_error):
actual = InactiveScore()(output)
assert np.all(actual == 0.0)
assert actual.shape == expected_shape
def test__call__if_score_is_(self, scores, expected_error, conv_model):
cam = Xcam(conv_model)
with assert_raises(expected_error):
result = cam(scores, dummy_sample((1, 8, 8, 3)))
assert result.shape == (1, 8, 8)
def test__call__(self, dense_model):
cam = Xcam(dense_model)
with assert_raises(ValueError):
result = cam(CategoricalScore(0), dummy_sample((1, 8, 8, 3)))
assert result.shape == (1, 8, 8)
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)
