def test_conv2d_mpc_mpc(get_clients, nr_clients, bias, stride,
padding) -> None:
clients = get_clients(nr_clients)
session = Session(parties=clients)
SessionManager.setup_mpc(session)
input_secret = torch.ones(1, 1, 4, 4)
weight_secret = torch.ones(1, 1, 2, 2)
input = MPCTensor(secret=input_secret, session=session)
weight = MPCTensor(secret=weight_secret, session=session)
kwargs = {"bias": bias, "stride": stride, "padding": padding}
result = input.conv2d(weight, **kwargs).reconstruct()
expected_result = torch.nn.functional.conv2d(input_secret, weight_secret,
**kwargs)
assert np.allclose(result, expected_result, rtol=10e-4)
def forward(self, x: MPCTensor) -> MPCTensor:
"""Do a feedforward through the layer.
Args:
x (MPCTensor): the input
Returns:
An MPCTensor representing the layer specific operation applied on the input
"""
res = x.conv2d(
weight=self.weight,
bias=self.bias,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=self.groups,
)
return res
