b7c005999921399e383780086127e4a4aa02b079,memcnn/models/additive.py,AdditiveBlockFunction,forward,#Any#Any#Any#Any#,92

Before Change

            x2.set_()
            del x2
            output = torch.cat([y1, y2], dim=1)
            y1.set_()
            y2.set_()
            del y1, y2

        // save the (empty) input and (non-empty) output variables
        ctx.save_for_backward(x.data, output)

After Change

        ctx.Gm = Gm

        with torch.no_grad():
            x = xin.detach()
            // partition in two equally sized set of channels
            x1, x2 = torch.chunk(x, 2, dim=1)
            x1, x2 = x1.contiguous(), x2.contiguous()

Instances