4a53e30fafdd8c65c42d0cf978312e6333182db4,modules/functions.py,InPlaceABN,forward,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,72

Before Change

        n = _count_samples(x)

        if ctx.training:
            mean = x.new().resize_as_(running_mean)
            var = x.new().resize_as_(running_var)
            _check_contiguous(x, mean, var)
            _check(_ext.bn_mean_var_cuda, x, mean, var)

            // Update running stats
            running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
            running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * n / (n - 1))

After Change

        // Prepare inputs
        count = _count_samples(x)
        x = x.contiguous()
        weight = weight.contiguous() if ctx.affine else x.new_empty(0)
        bias = bias.contiguous() if ctx.affine else x.new_empty(0)

        if ctx.training:
            mean, var = backend.mean_var(x)

Instances