def initialize_edges(x,
Rs_in,
pos,
edge_index_dict,
lmax,
self_edge=1.,
symmetric_edges=False):
"""Initialize edge features of DataEdgeNeighbors using node features and SphericalTensor.
Args:
x (torch.tensor shape [N, rs.dim(Rs_in)]): Node features.
Rs_in (rs.TY_RS_STRICT): Representation list of input.
pos (torch.tensor shape [N, 3]): Cartesian coordinates of nodes.
edge_index (torch.LongTensor shape [2, num_edges]): Edges described by index of node target then node source.
lmax (int > 0): Maximum L to use for SphericalTensor projection of radial distance vectors
self_edge (float, optional): L=0 feature for self edges. Defaults to 1.
symmetric_edges (bool, optional): Constrain edge features to be symmetric in node index. Defaults to False
Returns:
edge_x: Edge features.
Rs_edge (rs.TY_RS_STRICT): Representation list of edge features.
"""
from e3nn.tensor import SphericalTensor
edge_x = []
if symmetric_edges:
Rs, Q = rs.reduce_tensor('ij=ji', i=Rs_in)
else:
Rs, Q = rs.reduce_tensor('ij', i=Rs_in, j=Rs_in)
Q = Q.reshape(-1, rs.dim(Rs_in), rs.dim(Rs_in))
Rs_sph = [(1, l, (-1)**l) for l in range(lmax + 1)]
tp_kernel = rs.TensorProduct(Rs, Rs_sph, o3.selection_rule)
keys, values = list(zip(*edge_index_dict.items()))
sorted_edges = sorted(zip(keys, values), key=lambda x: x[1])
for (target, source), _ in sorted_edges:
Ia = x[target]
Ib = x[source]
vector = (pos[source] - pos[target]).reshape(-1, 3)
if torch.allclose(vector, torch.zeros(vector.shape)):
signal = torch.zeros(rs.dim(Rs_sph))
signal[0] = self_edge
else:
signal = SphericalTensor.from_geometry(vector, lmax=lmax).signal
if symmetric_edges:
signal += SphericalTensor.from_geometry(-vector,
lmax=lmax).signal
signal *= 0.5
output = torch.einsum('kij,i,j->k', Q, Ia, Ib)
output = tp_kernel(output, signal)
edge_x.append(output)
edge_x = torch.stack(edge_x, dim=0)
return edge_x, tp_kernel.Rs_out
def test_reduce_tensor_Levi_Civita_symbol():
Rs, Q = rs.reduce_tensor('ijk=-ikj=-jik', i=[(1, 1)])
assert Rs == [(1, 0, 0)]
r = o3.rand_angles()
D = o3.irr_repr(1, *r)
Q = Q.reshape(3, 3, 3)
Q1 = torch.einsum('li,mj,nk,ijk', D, D, D, Q)
assert (Q1 - Q).abs().max() < 1e-10
def to_irrep_transformation(self):
dim = self.tensor.dim()
change = o3.kron(*[o3.xyz_to_irreducible_basis()] * dim)
Rs = [(1, 1)] # vectors
old_indices = self.formula.split("=")[0]
Rs_out, Q = rs.reduce_tensor(self.formula,
**{i: Rs
for i in old_indices})
return Rs_out, torch.einsum('ab,bc->ac', Q,
change.reshape(3**dim, 3**dim))
def test_reduce_tensor_antisymmetric_L2():
Rs, Q = rs.reduce_tensor('ijk=-ikj=-jik', i=[(1, 2)])
assert Rs[0] == (1, 1, 0)
q = Q[:3].reshape(3, 5, 5, 5)
r = o3.rand_angles()
D1 = o3.irr_repr(1, *r)
D2 = o3.irr_repr(2, *r)
Q1 = torch.einsum('il,jm,kn,zijk->zlmn', D2, D2, D2, q)
Q2 = torch.einsum('yz,zijk->yijk', D1, q)
assert (Q1 - Q2).abs().max() < 1e-10
assert (q + q.transpose(1, 2)).abs().max() < 1e-10
assert (q + q.transpose(1, 3)).abs().max() < 1e-10
assert (q + q.transpose(3, 2)).abs().max() < 1e-10
def test_reduce_tensor_elasticity_tensor():
Rs, _Q = rs.reduce_tensor('ijkl=jikl=klij', i=[(1, 1)])
assert rs.dim(Rs) == 21
def test_reduce_tensor_elasticity_tensor_parity():
Rs, _Q = rs.reduce_tensor('ijkl=jikl=klij', i=[(1, 1, -1)])
assert all(p == 1 for (_, _, p) in Rs)
assert rs.dim(Rs) == 21
