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64c992d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import torch
@torch.jit.script
def gaussian(x, mean, std):
pi = 3.14159
a = (2*pi) ** 0.5
return torch.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std)
# From Graphormer
class GaussianRadialBasisLayer(torch.nn.Module):
def __init__(self, num_basis, cutoff):
super().__init__()
self.num_basis = num_basis
self.cutoff = cutoff + 0.0
self.mean = torch.nn.Parameter(torch.zeros(1, self.num_basis))
self.std = torch.nn.Parameter(torch.zeros(1, self.num_basis))
self.weight = torch.nn.Parameter(torch.ones(1, 1))
self.bias = torch.nn.Parameter(torch.zeros(1, 1))
self.std_init_max = 1.0
self.std_init_min = 1.0 / self.num_basis
self.mean_init_max = 1.0
self.mean_init_min = 0
torch.nn.init.uniform_(self.mean, self.mean_init_min, self.mean_init_max)
torch.nn.init.uniform_(self.std, self.std_init_min, self.std_init_max)
torch.nn.init.constant_(self.weight, 1)
torch.nn.init.constant_(self.bias, 0)
def forward(self, dist, node_atom=None, edge_src=None, edge_dst=None):
x = dist / self.cutoff
x = x.unsqueeze(-1)
x = self.weight * x + self.bias
x = x.expand(-1, self.num_basis)
mean = self.mean
std = self.std.abs() + 1e-5
x = gaussian(x, mean, std)
return x
def extra_repr(self):
return 'mean_init_max={}, mean_init_min={}, std_init_max={}, std_init_min={}'.format(
self.mean_init_max, self.mean_init_min, self.std_init_max, self.std_init_min)
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