def test_SO3Vec_cat(self, batch1, batch2, batch3, channels1, channels2, channels3, maxl1, maxl2, maxl3):
tau1 = [channels1] * (maxl1+1)
tau2 = [channels2] * (maxl2+1)
tau3 = [channels3] * (maxl2+1)
tau12 = SO3Tau.cat([tau1, tau2])
tau123 = SO3Tau.cat([tau1, tau2, tau3])
vec1 = SO3Vec.randn(tau1, batch1)
vec2 = SO3Vec.randn(tau2, batch2)
vec3 = SO3Vec.randn(tau3, batch3)
if batch1 == batch2:
vec12 = so3_torch.cat([vec1, vec2])
assert vec12.tau == tau12
else:
with pytest.raises(RuntimeError):
vec12 = so3_torch.cat([vec1, vec2])
if batch1 == batch2 == batch3:
vec123 = so3_torch.cat([vec1, vec2, vec3])
assert vec123.tau == tau123
else:
with pytest.raises(RuntimeError):
vec12 = so3_torch.cat([vec1, vec2, vec3])
def test_CGProduct(self, batch, maxl1, maxl2, maxl, channels):
maxl_all = max(maxl1, maxl2, maxl)
D, R, _ = rot.gen_rot(maxl_all)
cg_dict = CGDict(maxl=maxl_all, dtype=torch.double)
cg_prod = CGProduct(maxl=maxl, dtype=torch.double, cg_dict=cg_dict)
tau1 = SO3Tau([channels] * (maxl1 + 1))
tau2 = SO3Tau([channels] * (maxl2 + 1))
vec1 = SO3Vec.randn(tau1, batch, dtype=torch.double)
vec2 = SO3Vec.randn(tau2, batch, dtype=torch.double)
vec1i = vec1.apply_wigner(D, dir='left')
vec2i = vec2.apply_wigner(D, dir='left')
vec_prod = cg_prod(vec1, vec2)
veci_prod = cg_prod(vec1i, vec2i)
vecf_prod = vec_prod.apply_wigner(D, dir='left')
# diff = (sph_harmsr - sph_harmsd).abs()
diff = [(p1 - p2).abs().max() for p1, p2 in zip(veci_prod, vecf_prod)]
assert all([d < 1e-6 for d in diff])