def test_gradient_calculator_multiply_target():
model = DummyModel()
x = numpy.array([[1, 5, 8]], dtype=numpy.float32)
calculator = GradientCalculator(model, multiply_target=True)
saliency = calculator.compute(x)
# gradient * input
assert numpy.allclose(saliency, numpy.array([[1, 15, 16]]))
def test_gradient_calculator():
model = DummyModel()
x = numpy.array([[1, 5, 8]], dtype=numpy.float32)
calculator = GradientCalculator(model)
saliency = calculator.compute(x)
# Gradient is equal to `initialW` of DummyModel.
assert numpy.allclose(saliency, numpy.array([[1, 3, 2]]))
def test_gradient_calculator_multiply_target():
model = DummyModel()
x = numpy.array([[1, 5, 8]], dtype=numpy.float32)
calculator = GradientCalculator(model, multiply_target=True)
saliency = calculator.compute(x)
# gradient * input
assert numpy.allclose(saliency, numpy.array([[1, 15, 16]]))
def test_gradient_calculator():
model = DummyModel()
x = numpy.array([[1, 5, 8]], dtype=numpy.float32)
calculator = GradientCalculator(model)
saliency = calculator.compute(x)
# Gradient is equal to `initialW` of DummyModel.
assert numpy.allclose(saliency, numpy.array([[1, 3, 2]]))
def test_gradient_calculator_multiple_output():
model = DummyModel()
x = numpy.array([[1, 5, 8], [2, 3, 4]], dtype=numpy.float32)
calculator = GradientCalculator(model)
# even batchsize=2 sum is applied automatically inside `compute`,
# so gradient can be calculated.
saliency = calculator.compute(x)
# Gradient is equal to `initialW` of DummyModel.
assert numpy.allclose(saliency, numpy.array([[1, 3, 2]]))
def test_gradient_calculator_target_extractor():
model = DummyModel()
x = numpy.array([[1, 5, 8]], dtype=numpy.float32)
calculator = GradientCalculator(
model,
target_extractor=VariableMonitorLinkHook(model.l1, timing='pre'))
saliency = calculator.compute(x)
# Gradient is equal to `initialW` of DummyModel.
assert numpy.allclose(saliency, numpy.array([[[1, 3, 2]]]))
assert saliency.shape == (1, 1, 3)
def test_gradient_calculator_target_extractor():
model = DummyModel()
x = numpy.array([[1, 5, 8]], dtype=numpy.float32)
calculator = GradientCalculator(
model,
target_extractor=VariableMonitorLinkHook(model.l1, timing='pre'))
saliency = calculator.compute(x)
# Gradient is equal to `initialW` of DummyModel.
assert numpy.allclose(saliency, numpy.array([[[1, 3, 2]]]))
assert saliency.shape == (1, 1, 3)