def test_smear_gaussian_trainable(): dist = torch.tensor([[[0.0, 1.0, 1.5, 0.25], [0.5, 1.5, 3.0, 1.0]]]) # smear using 5 Gaussian functions with 0.75 spacing smear = GaussianSmearing(start=1., stop=4., n_gaussians=5, trainable=True) # absolute value of centered distances expt = torch.tensor([[[[1, 1.75, 2.5, 3.25, 4.], [0, 0.75, 1.5, 2.25, 3.], [0.5, 0.25, 1., 1.75, 2.5], [0.75, 1.5, 2.25, 3., 3.75]], [[0.5, 1.25, 2., 2.75, 3.5], [0.5, 0.25, 1., 1.75, 2.5], [2., 1.25, 0.5, 0.25, 1.], [0, 0.75, 1.5, 2.25, 3.]]]]) expt = torch.exp((-0.5 / 0.75**2) * expt**2) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) params = list(smear.parameters()) assert len(params) == 2 assert len(params[0]) == 5 assert len(params[1]) == 5 # centered = True smear = GaussianSmearing(start=1., stop=4., n_gaussians=5, trainable=True, centered=True) expt = -0.5 / torch.tensor([1, 1.75, 2.5, 3.25, 4])**2 expt = torch.exp(expt * dist[:, :, :, None]**2) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) params = list(smear.parameters()) assert len(params) == 2 assert len(params[0]) == 5 assert len(params[1]) == 5
def test_smear_gaussian(): dist = torch.tensor([[[0.0, 1.0, 1.5], [0.5, 1.5, 3.0]]]) # smear using 4 Gaussian functions with 1. spacing smear = GaussianSmearing(start=1., stop=4., n_gaussians=4) # absolute value of centered distances expt = torch.tensor([[[[1, 2, 3, 4], [0, 1, 2, 3], [0.5, 0.5, 1.5, 2.5]], [[.5, 1.5, 2.5, 3.5], [.5, .5, 1.5, 2.5], [2, 1, 0, 1]]]]) expt = torch.exp(-0.5 * expt**2) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) assert list(smear.parameters()) == [] # centered = True smear = GaussianSmearing(start=1., stop=4., n_gaussians=4, centered=True) expt = torch.exp((-0.5 / torch.tensor([1, 2, 3, 4.])**2) * dist[:, :, :, None]**2) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) assert list(smear.parameters()) == []
def test_smear_gaussian_one_distance(): # case of one distance dist = torch.tensor([[[1.0]]]) # trainable = False smear = GaussianSmearing(n_gaussians=6, centered=False, trainable=False) expt = torch.exp(-0.5 * torch.tensor([[[1., 0., 1., 4., 9., 16.]]])) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) assert list(smear.parameters()) == [] # trainable = True smear = GaussianSmearing(n_gaussians=6, centered=False, trainable=True) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) params = list(smear.parameters()) assert len(params) == 2 assert len(params[0]) == 6 assert len(params[1]) == 6 # centered = True smear = GaussianSmearing(n_gaussians=6, centered=True) expt = -0.5 / torch.tensor([0., 1, 2, 3, 4, 5])**2 expt = torch.exp(expt * dist[:, :, :, None]**2) assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7) assert list(smear.parameters()) == []