def test_digitise():
x = torch.tensor([12345])
digits = FalconHelper.digitise(x)
reversed_digits = FalconHelper.digitise(x, True)
assert type(digits) is list
assert (digits[0] == torch.tensor(1)).all()
assert (reversed_digits[0] == torch.tensor(5)).all()
def test_private_xor(workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
x = torch.tensor([0]).share(bob, alice, james, protocol="falcon", field=2)
y = torch.tensor([1]).share(bob, alice, james, protocol="falcon", field=2)
assert (FalconHelper.xor(x, y).reconstruct() == torch.tensor(1)).all()
assert (FalconHelper.xor(x, x).reconstruct() == torch.tensor(0)).all()
assert (FalconHelper.xor(y, y).reconstruct() == torch.tensor(0)).all()
assert (FalconHelper.xor(y, x).reconstruct() == torch.tensor(1)).all()
def test_determine_sign(workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
workers = [bob, alice, james]
x = torch.tensor([-4, 5, 6]).share(*workers, protocol="falcon")
beta_0 = torch.tensor(0).share(*workers, protocol="falcon", field=2)
beta_1 = torch.tensor(1).share(*workers, protocol="falcon", field=2)
assert (FalconHelper.determine_sign(
x, beta_0).reconstruct() == torch.tensor([-4, 5, 6])).all()
assert (FalconHelper.determine_sign(
x, beta_1).reconstruct() == (-1 * torch.tensor([-4, 5, 6]))).all()
def test_select_share(workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
workers = [bob, alice, james]
x = torch.tensor([0, 1, 2]).share(*workers, protocol="falcon")
y = torch.tensor([-3, 0, 1]).share(*workers, protocol="falcon")
b_0 = torch.tensor(0).share(*workers, protocol="falcon", field=2)
b_1 = torch.tensor(1).share(*workers, protocol="falcon", field=2)
assert (FalconHelper.select_shares(
b_0, x, y).reconstruct() == torch.tensor([0, 1, 2])).all()
assert (FalconHelper.select_shares(
b_1, x, y).reconstruct() == torch.tensor([-3, 0, 1])).all()
def test_determine_sign(beta, workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
workers = [bob, alice, james]
x = torch.tensor([-4, 5, 6])
x_shared = x.share(*workers, protocol="falcon")
ring_size = x_shared.ring_size
shape = x_shared.shape
expected_plaintext = (-1)**beta * x
if beta:
beta = torch.ones(size=shape,
dtype=torch.long).share(*workers,
protocol="falcon",
field=ring_size)
else:
beta = torch.zeros(size=shape,
dtype=torch.long).share(*workers,
protocol="falcon",
field=ring_size)
plaintext = FalconHelper.determine_sign(x_shared, beta).reconstruct()
assert (expected_plaintext == plaintext).all()
def conv2d(filters: syft.ReplicatedSharingTensor, image, padding=0):
image_batches, image_channels, image_width, image_height = image.shape
channels_out, filter_channels, filter_width, filter_height = filters.shape
image = FalconHelper.unfold(image, filter_height, padding)
filters = filters.view(channels_out, -1)
result = filters @ image
output_size = (image_height - filter_height + 2 * padding) + 1
result = result.view(-1, channels_out, output_size, output_size)
return result
def test_select_share(bit_select, workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
workers = [bob, alice, james]
x = torch.tensor([0, 1, 2])
x_shared = x.share(*workers, protocol="falcon")
y = torch.tensor([-3, 0, 1])
y_shared = y.share(*workers, protocol="falcon")
b_shared = torch.tensor(bit_select).share(*workers,
protocol="falcon",
field=2)
plaintext = FalconHelper.select_share(b_shared, x_shared,
y_shared).reconstruct()
if bit_select:
assert (plaintext == y).all()
else:
assert (plaintext == x).all()
def test_private_xor(x_val, y_val, x_xor_y, workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
x = x_val.share(bob, alice, james, protocol="falcon", field=2)
y = y_val.share(bob, alice, james, protocol="falcon", field=2)
assert (FalconHelper.xor(x, y).reconstruct() == x_xor_y).all()