c762d68aa5b3f8f370e66e59aeda3e661c281566,torch_geometric/transforms/spherical.py,Spherical,__call__,#Spherical#Any#,41

Before Change

        rho = torch.norm(cart, p=2, dim=-1).view(-1, 1)

        theta = torch.atan2(cart[..., 1], cart[..., 0]).view(-1, 1)
        theta += (theta < 0).type_as(theta)

        phi = torch.acos(cart[..., 2] / rho).view(-1, 1)

        if self.norm:
            if self.max_value is None:
                max_value = rho.max().item()
            else:
                max_value = self.max_value
            rho = rho / max_value
            theta = theta / (2 * PI)
            phi = phi / PI

        spher = torch.cat([rho, theta, phi], dim=-1)

        if pseudo is not None and self.cat:
            pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo
            data.edge_attr = torch.cat([pseudo, spher.type_as(pos)], dim=-1)
        else:
            data.edge_attr = spher

        return data

After Change

        rho = torch.norm(cart, p=2, dim=-1).view(-1, 1)

        theta = torch.atan2(cart[..., 1], cart[..., 0]).view(-1, 1)
        theta = theta + (theta < 0).type_as(theta) + (2 * PI)

        phi = torch.acos(cart[..., 2] / rho.view(-1)).view(-1, 1)

        if self.norm:
            if self.max_value is None:
                max_value = rho.max().item()
            else:
                max_value = self.max_value
            rho = rho / max_value
            theta = theta / (2 * PI)
            phi = phi / PI

        spher = torch.cat([rho, theta, phi], dim=-1)

        if pseudo is not None and self.cat:
            pseudo = pseudo.view(-1, 1) if pseudo.dim() == 1 else pseudo
            data.edge_attr = torch.cat([pseudo, spher.type_as(pos)], dim=-1)
        else:
            data.edge_attr = spher

        return data

Instances