def forward()

in Dassl.pytorch/dassl/modeling/ops/mixstyle.py [0:0]

• 27 lines of code
• 7 McCabe index (conditional complexity)

    def forward(self, x):
        if not self.training or not self._activated:
            return x

        if random.random() > self.p:
            return x

        B = x.size(0)

        mu = x.mean(dim=[2, 3], keepdim=True)
        var = x.var(dim=[2, 3], keepdim=True)
        sig = (var + self.eps).sqrt()
        mu, sig = mu.detach(), sig.detach()
        x_normed = (x-mu) / sig

        lmda = self.beta.sample((B, 1, 1, 1))
        lmda = lmda.to(x.device)

        if self.mix == "random":
            # random shuffle
            perm = torch.randperm(B)

        elif self.mix == "crossdomain":
            # split into two halves and swap the order
            perm = torch.arange(B - 1, -1, -1)  # inverse index
            perm_b, perm_a = perm.chunk(2)
            perm_b = perm_b[torch.randperm(perm_b.shape[0])]
            perm_a = perm_a[torch.randperm(perm_a.shape[0])]
            perm = torch.cat([perm_b, perm_a], 0)

        else:
            raise NotImplementedError

        mu2, sig2 = mu[perm], sig[perm]
        mu_mix = mu*lmda + mu2 * (1-lmda)
        sig_mix = sig*lmda + sig2 * (1-lmda)

        return x_normed*sig_mix + mu_mix