def hardtanh(tensor: MPCTensor,
min_value: float = -1,
max_value: float = 1) -> MPCTensor:
"""Calculates hardtanh of given tensor.
Defined as
1 if x > 1
-1 if x < -1
x otherwise
Args:
tensor (MPCTensor): whose hardtanh has to be calculated
min_value (float): minimum value of the linear region range. Default: -1
max_value (float): maximum value of the linear region range. Default: 1
Returns:
MPCTensor: calculated MPCTensor
"""
intermediate = relu(tensor - min_value) - relu(tensor - max_value)
return intermediate + min_value
def test_relu(get_clients) -> None:
clients = get_clients(2)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
secret = torch.Tensor([-2, -1.5, 0, 1, 1.5, 2])
mpc_tensor = MPCTensor(secret=secret, session=session)
res = relu(mpc_tensor)
res_expected = torch.nn.functional.relu(secret)
assert all(res.reconstruct() == res_expected)