def test1(self):
"""test gradients of the Kernel"""
torch.set_default_dtype(torch.float64)
Rs_in = [(1, 0), (1, 1), (1, 0), (1, 2)]
Rs_out = [(1, 0), (1, 1), (1, 2), (1, 0)]
kernel = Kernel(Rs_in, Rs_out, ConstantRadialModel,
partial(o3.selection_rule_in_out_sh, lmax=1))
n_path = 0
for mul_out, l_out, p_out in kernel.Rs_out:
for mul_in, l_in, p_in in kernel.Rs_in:
l_filters = kernel.selection_rule(l_in, p_in, l_out, p_out)
n_path += mul_out * mul_in * len(l_filters)
r = torch.randn(2, 3)
Y = rsh.spherical_harmonics_xyz(kernel.set_of_l_filters,
r) # [l_filter * m_filter, batch]
Y = Y.clone().detach().requires_grad_(True)
R = torch.randn(
2, n_path, requires_grad=True
) # [batch, l_out * l_in * mul_out * mul_in * l_filter]
inputs = (Y, R, kernel.norm_coef, kernel.Rs_in, kernel.Rs_out,
kernel.selection_rule, kernel.set_of_l_filters)
self.assertTrue(torch.autograd.gradcheck(KernelFn.apply, inputs))
def test1(self):
torch.set_default_dtype(torch.float64)
Rs_in = [(1, 0), (1, 1), (2, 0), (1, 2)]
Rs_out = [(2, 0), (1, 1), (1, 2), (3, 0)]
kernel = Kernel(Rs_in, Rs_out, ConstantRadialModel)
n_path = 0
for mul_out, l_out, p_out in kernel.Rs_out:
for mul_in, l_in, p_in in kernel.Rs_in:
l_filters = kernel.get_l_filters(l_in, p_in, l_out, p_out)
n_path += mul_out * mul_in * len(l_filters)
for rg_Y, rg_R in [(True, True), (True, False), (False, True)]:
r = torch.randn(2, 3)
radii = r.norm(2, dim=1) # [batch]
Y = kernel.sh(kernel.set_of_l_filters,
r) # [l_filter * m_filter, batch]
Y = Y.clone().detach().requires_grad_(rg_Y)
R = torch.randn(
2, n_path, requires_grad=rg_R
) # [batch, l_out * l_in * mul_out * mul_in * l_filter]
norm_coef = kernel.norm_coef
norm_coef = norm_coef[:, :, (radii == 0).type(
torch.long)] # [l_out, l_in, batch]
inputs = (Y, R, norm_coef, kernel.Rs_in, kernel.Rs_out,
kernel.get_l_filters, kernel.set_of_l_filters)
self.assertTrue(torch.autograd.gradcheck(KernelFn.apply, inputs))
def test_compare_forward(self):
for normalization in ["norm", "component"]:
torch.manual_seed(0)
K = Kernel(self.Rs_in, self.Rs_out, RadialModel=ConstantRadialModel, normalization=normalization)
new_features = K(self.geometry)
torch.manual_seed(0)
K2 = Kernel(self.Rs_in, self.Rs_out, RadialModel=ConstantRadialModel, normalization=normalization)
check_new_features = K2(self.geometry, custom_backward=True)
assert all(torch.all(a == b) for a, b in zip(K.parameters(), K.parameters())), self.msg
self.assertTrue(torch.allclose(new_features, check_new_features))
def test_flow():
"""
This test checks that information is flowing as expected from target to source.
edge_index[0] is source (convolution center)
edge_index[1] is target (neighbors)
"""
edge_index = torch.LongTensor([
[0, 0, 0, 0],
[1, 2, 3, 4],
])
features = torch.tensor([-1., 1., 1., 1., 1.])
features = features.unsqueeze(-1)
edge_r = torch.ones(edge_index.shape[-1], 3)
Rs = [0]
conv = Convolution(Kernel(Rs, Rs, ConstantRadialModel))
conv.kernel.R.weight.data.fill_(1.) # Fix weight to 1.
output = conv(features, edge_index, edge_r)
torch.allclose(output, torch.tensor([4., 0., 0., 0., 0.]).unsqueeze(-1))
edge_index = torch.LongTensor([[1, 2, 3, 4], [0, 0, 0, 0]])
output = conv(features, edge_index, edge_r)
torch.allclose(output,
torch.tensor([0., -1., -1., -1., -1.]).unsqueeze(-1))
def test1(self):
with torch_default_dtype(torch.float64):
Rs_in = [(3, 0), (3, 1), (2, 0), (1, 2)]
Rs_out = [(3, 0), (3, 1), (1, 2), (3, 0)]
f = GatedBlock(Rs_out, rescaled_act.Softplus(beta=5),
rescaled_act.sigmoid)
c = Convolution(Kernel(Rs_in, f.Rs_in, ConstantRadialModel))
abc = torch.randn(3)
D_in = o3.direct_sum(
*
[o3.irr_repr(l, *abc) for mul, l in Rs_in for _ in range(mul)])
D_out = o3.direct_sum(*[
o3.irr_repr(l, *abc) for mul, l in Rs_out for _ in range(mul)
])
x = torch.randn(1, 5, sum(mul * (2 * l + 1) for mul, l in Rs_in))
geo = torch.randn(1, 5, 3)
rx = torch.einsum("ij,zaj->zai", (D_in, x))
rgeo = geo @ o3.rot(*abc).t()
y = f(c(x, geo), dim=2)
ry = torch.einsum("ij,zaj->zai", (D_out, y))
self.assertLess((f(c(rx, rgeo)) - ry).norm(), 1e-10 * ry.norm())
def __init__(self, Rs_in, Rs_out):
super().__init__()
# hack: use Kernel to construct the weight matrix
def get_l_filters(l_in, l_out):
return [0] if l_in == l_out else []
self.kernel = Kernel(Rs_in, Rs_out, ConstantRadialModel, get_l_filters)
def __init__(self, Rs_in, Rs_out, size, **kwargs):
super().__init__()
R = partial(CosineBasisModel, max_radius=1.0, number_of_basis=(size + 1) // 2, h=50, L=3, act=rescaled_act.relu)
self.kernel = Kernel(Rs_in, Rs_out, R, normalization='component')
x = torch.linspace(-1, 1, size)
self.r = torch.stack(torch.meshgrid(x, x, x), dim=-1)
self.kwargs = kwargs
def test_equivariance(Rs_in, Rs_out, n_source, n_target, n_edge):
torch.set_default_dtype(torch.float64)
mp = Convolution(Kernel(Rs_in, Rs_out, ConstantRadialModel))
groups = 4
mp_group = Convolution(
GroupKernel(Rs_in, Rs_out,
partial(Kernel, RadialModel=ConstantRadialModel), groups))
features = rs.randn(n_target, Rs_in)
features2 = rs.randn(n_target, Rs_in * groups)
r_source = torch.randn(n_source, 3)
r_target = torch.randn(n_target, 3)
edge_index = torch.stack([
torch.randint(n_source, size=(n_edge, )),
torch.randint(n_target, size=(n_edge, )),
])
size = (n_target, n_source)
if n_edge == 0:
edge_r = torch.zeros(0, 3)
else:
edge_r = torch.stack(
[r_target[j] - r_source[i] for i, j in edge_index.T])
print(features.shape, edge_index.shape, edge_r.shape, size)
out1 = mp(features, edge_index, edge_r, size=size)
out1_groups = mp(features2, edge_index, edge_r, size=size, groups=groups)
out1_kernel_groups = mp_group(features2,
edge_index,
edge_r,
size=size,
groups=groups)
angles = o3.rand_angles()
D_in = rs.rep(Rs_in, *angles)
D_out = rs.rep(Rs_out, *angles)
D_in_groups = rs.rep(Rs_in * groups, *angles)
D_out_groups = rs.rep(Rs_out * groups, *angles)
R = o3.rot(*angles)
out2 = mp(features @ D_in.T, edge_index, edge_r @ R.T, size=size) @ D_out
out2_groups = mp(features2 @ D_in_groups.T,
edge_index,
edge_r @ R.T,
size=size,
groups=groups) @ D_out_groups
out2_kernel_groups = mp_group(features2 @ D_in_groups.T,
edge_index,
edge_r @ R.T,
size=size,
groups=groups) @ D_out_groups
assert (out1 - out2).abs().max() < 1e-10
assert (out1_groups - out2_groups).abs().max() < 1e-10
assert (out1_kernel_groups - out2_kernel_groups).abs().max() < 1e-10
def test2(self):
with torch_default_dtype(torch.float64):
mul = 100000
for l_in in range(4):
Rs_in = [(mul, l_in)]
for l_out in range(4):
Rs_out = [(1, l_out)]
k = Kernel(Rs_in,
Rs_out,
ConstantRadialModel,
normalization='norm')
k = k(torch.randn(1, 3))
self.assertLess(k.mean().item(), 1e-3)
self.assertAlmostEqual(k.var().item() * mul,
1 / (2 * l_out + 1),
places=1)
def __init__(self, num_radial=30, max_radius=2):
super().__init__()
sp = rescaled_act.Softplus(beta=5)
RadialModel = partial(CosineBasisModel,
max_radius=max_radius,
number_of_basis=num_radial,
h=100,
L=2,
act=sp)
self.conv = Convolution(Kernel([(1, 0)], [(1, 1)], RadialModel))
def test_equivariance():
torch.set_default_dtype(torch.float64)
n_edge = 3
n_source = 4
n_target = 2
Rs_in = [(3, 0), (0, 1)]
Rs_mid1 = [(5, 0), (1, 1)]
Rs_mid2 = [(5, 0), (1, 1), (1, 2)]
Rs_out = [(5, 1), (3, 2)]
convolution = lambda Rs_in, Rs_out: Convolution(Kernel(Rs_in, Rs_out, ConstantRadialModel))
convolution_groups = lambda Rs_in, Rs_out: Convolution(
GroupKernel(Rs_in, Rs_out, partial(Kernel, RadialModel=ConstantRadialModel), groups))
groups = 4
mp = DepthwiseConvolution(Rs_in, Rs_out, Rs_mid1, Rs_mid2, groups, convolution)
mp_groups = DepthwiseConvolution(Rs_in, Rs_out, Rs_mid1, Rs_mid2, groups, convolution_groups)
features = rs.randn(n_target, Rs_in)
r_source = torch.randn(n_source, 3)
r_target = torch.randn(n_target, 3)
edge_index = torch.stack([
torch.randint(n_source, size=(n_edge,)),
torch.randint(n_target, size=(n_edge,)),
])
size = (n_target, n_source)
if n_edge == 0:
edge_r = torch.zeros(0, 3)
else:
edge_r = torch.stack([
r_target[j] - r_source[i]
for i, j in edge_index.T
])
print(features.shape, edge_index.shape, edge_r.shape, size)
out1 = mp(features, edge_index, edge_r, size=size)
out1_groups = mp_groups(features, edge_index, edge_r, size=size)
angles = o3.rand_angles()
D_in = rs.rep(Rs_in, *angles)
D_out = rs.rep(Rs_out, *angles)
R = o3.rot(*angles)
out2 = mp(features @ D_in.T, edge_index, edge_r @ R.T, size=size) @ D_out
out2_groups = mp_groups(features @ D_in.T, edge_index, edge_r @ R.T, size=size) @ D_out
assert (out1 - out2).abs().max() < 1e-10
assert (out1_groups - out2_groups).abs().max() < 1e-10
def get_kernel_conv_kernelconv(self, seed, normalization):
torch.manual_seed(seed)
C = Convolution(
Kernel(self.Rs_in,
self.Rs_out,
ConstantRadialModel,
normalization=normalization))
torch.manual_seed(seed)
KC = KernelConv(self.Rs_in,
self.Rs_out,
RadialModel=ConstantRadialModel,
normalization=normalization)
return C, KC
def layer(Rs1, Rs2):
R = partial(GaussianRadialModel,
max_radius=max_radius,
number_of_basis=radial_basis,
h=radial_neurons,
L=radial_layers,
act=radial_act,
min_radius=min_radius)
k = Kernel(Rs1,
Rs2,
R,
partial(o3.selection_rule_in_out_sh, lmax=lmax),
allow_unused_inputs=True)
return Convolution(k)
def test2(self):
"""Test rotation equivariance on Kernel."""
with torch_default_dtype(torch.float64):
Rs_in = [(2, 0), (0, 1), (2, 2)]
Rs_out = [(2, 0), (2, 1), (2, 2)]
k = Kernel(Rs_in, Rs_out, ConstantRadialModel)
r = torch.randn(3)
abc = torch.randn(3)
D_in = o3.direct_sum(
*
[o3.irr_repr(l, *abc) for mul, l in Rs_in for _ in range(mul)])
D_out = o3.direct_sum(*[
o3.irr_repr(l, *abc) for mul, l in Rs_out for _ in range(mul)
])
W1 = D_out @ k(r) # [i, j]
W2 = k(o3.rot(*abc) @ r) @ D_in # [i, j]
self.assertLess((W1 - W2).norm(), 10e-5 * W1.norm())
def test4(self):
"""Test parity equivariance on Kernel."""
with torch_default_dtype(torch.float64):
Rs_in = [(2, 0, 1), (2, 1, 1), (2, 2, -1)]
Rs_out = [(2, 0, -1), (2, 1, 1), (2, 2, 1)]
k = Kernel(Rs_in, Rs_out, ConstantRadialModel)
r = torch.randn(3)
D_in = o3.direct_sum(*[
p * torch.eye(2 * l + 1) for mul, l, p in Rs_in
for _ in range(mul)
])
D_out = o3.direct_sum(*[
p * torch.eye(2 * l + 1) for mul, l, p in Rs_out
for _ in range(mul)
])
W1 = D_out @ k(r) # [i, j]
W2 = k(-r) @ D_in # [i, j]
self.assertLess((W1 - W2).norm(), 10e-5 * W1.norm())
