f66d3c5ed7b7fa96424fd0d8214594042d9ba3ff,se3cnn/SO3.py,,irr_repr,#,81

Before Change

    
    from lie_learn.representations.SO3.wigner_d import wigner_D_matrix
    if torch.is_tensor(alpha):
        alpha = alpha.item()
    if torch.is_tensor(beta):
        beta = beta.item()
    if torch.is_tensor(gamma):
        gamma = gamma.item()
    return torch.tensor(wigner_D_matrix(order, alpha, beta, gamma), dtype=torch.get_default_dtype() if dtype is None else dtype)



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After Change

    - compatible with compose and spherical_harmonics
    
    from lie_learn.representations.SO3.wigner_d import wigner_D_matrix
    abc = [alpha, beta, gamma]
    for i, x in enumerate(abc):
        if torch.is_tensor(x):
            abc[i] = x.item()
            if dtype is None:
                dtype = x.dtype
            if device is None:
                device = x.device
    if dtype is None:
        dtype = torch.get_default_dtype()
    return torch.tensor(wigner_D_matrix(order, *abc), dtype=dtype, device=device)



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Instances