def forward(self, x):
x = x * 0.5
x.requires_grad_(True)
h = self.conv(x)
grad_x = grad(
h,
(x,),
retain_graph=True,
create_graph=True,
)
h = self.linear(grad_x[0])
return h
def forward(self, x):
x0 = x * 0.5
x0.requires_grad_(True)
h = self.conv(x0)
grad_x0 = grad(
h,
(x0,),
retain_graph=True,
create_graph=True,
)
x1 = x * 0.5
x1.requires_grad_(True)
h = self.conv(x1)
grad_x1 = grad(
h,
(x1,),
retain_graph=True,
create_graph=True,
)
h = self.linear(grad_x0[0] + grad_x1[0])
return h
def forward(self, x):
x0 = x * 0.5
x1 = x * 2.0
x0.requires_grad_(True)
x1.requires_grad_(True)
h = self.conv(torch.cat([x0, x1], dim=1))
grad_x = grad(
h,
(x0, x1),
retain_graph=True,
create_graph=True,
)
h = self.linear(grad_x[0])
return h