cf767a1297d67caf41577508aacc7cbc24ae8f14,scripts/gan/stylegan/modules.py,EqualLinear,hybrid_forward,#EqualLinear#Any#Any#,116

Before Change

        new_weight = compute_weight(kwargs["weight_orig"])
        self.linear.params[self.weight_key].set_data(new_weight)

        return self.linear(x)


class AdaptiveInstanceNorm(nn.HybridBlock):
    def __init__(self, in_channel, style_dim):

After Change

    def hybrid_forward(self, F, x, **kwargs):

        size = kwargs["weight"].shape
        fan_in = prod(size[1:])
        multiplier = sqrt(2.0 / fan_in)

        out = F.FullyConnected(x, kwargs["weight"]*multiplier, kwargs["bias"], num_hidden=self.num_hidden)

        return out


class AdaptiveInstanceNorm(nn.HybridBlock):
    def __init__(self, in_channel, style_dim):

Instances