def forward(self, x, y):
x, y = torch.distributions.utils.broadcast_all(x, y)
x, unflatten = util.flatten_batch(x, 1)
y, _ = util.flatten_batch(y, 1)
output = self.net(torch.abs(x - y))
return unflatten(output)
def forward(self, x, y):
x, y = torch.distributions.utils.broadcast_all(x, y)
x, unflatten = util.flatten_batch(x, 1)
y, _ = util.flatten_batch(y, 1)
output = -torch.mean(torch.abs(x - y), dim=-1, keepdim=True)
output = self.adjust(output)
return unflatten(output)
def forward(self, x, y):
x, y = torch.distributions.utils.broadcast_all(x, y)
x, unflatten = util.flatten_batch(x, 1)
y, _ = util.flatten_batch(y, 1)
xy = torch.sum(x * y, dim=-1, keepdim=True)
xx = torch.sum(x**2, dim=-1, keepdim=True)
yy = torch.sum(y**2, dim=-1, keepdim=True)
eps = 1e-3
output = xy / (torch.sqrt(xx) * torch.sqrt(yy) + eps)
output = self.adjust(output)
return unflatten(output)