def test_wrong_percentage_perturbation(self):
size = 3
mp = DropoutMask((size, ))
for i in [1.1, 1.5, 3.1]:
mp.perturb(amount=i)
assert pnp.sum(mp.mask) == round(i)
mp.clear()
def test_percentage_perturbation(self):
size = 3
mp = DropoutMask((size, ))
for i in [0.01, 0.1, 0.5, 0.9]:
mp.perturb(amount=i)
assert pnp.sum(mp.mask) == round(i * mp.mask.size)
mp.clear()
def test_perturbation(self):
size = 3
mp = DropoutMask((size, ))
for i in range(1, size + 1):
mp.perturb(i)
mp.perturb(i, mode=PerturbationMode.RESET)
assert pnp.sum(mp.mask) == 0
def test_perturbation_mode(self, mode):
size = 3
mp = DropoutMask((size, ))
for amount in [0, size, size + 1]:
mp.perturb(amount=amount, mode=mode[0])
mp.perturb(amount=amount, mode=mode[1])
assert pnp.sum(mp.mask) == 0
def test_perturbation_add_remove(self):
size = 3
mp = DropoutMask((size, ))
for amount in [random.randrange(size), 0, size, size + 1]:
mp.perturb(amount=amount, mode=PerturbationMode.SET)
assert pnp.sum(mp.mask) == min(amount, size)
mp.perturb(amount=amount, mode=PerturbationMode.RESET)
assert pnp.sum(mp.mask) == 0
def test_perturbation_invert_remove(self):
size = 3
mp = DropoutMask((size, ))
for amount in [random.randrange(size), 0, size, size + 1]:
mp.perturb(amount=amount, mode=PerturbationMode.INVERT)
reversed_amount = pnp.sum(mp.mask).unwrap() # unwrap tensor
mp.perturb(amount=reversed_amount, mode=PerturbationMode.RESET)
assert pnp.sum(mp.mask) == 0
def test_negative_perturbation(self):
mp = DropoutMask((3, ))
with pytest.raises(AssertionError):
mp.perturb(amount=-1)
def test_wrong_mode(self):
mp = DropoutMask((3, ))
with pytest.raises(NotImplementedError):
mp.perturb(mode=10, amount=1)