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added alpha scaling in expnorm and visualization function #30

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Jul 19, 2021
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added alpha scaling in expnorm and visualization function #30

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37483fa
added alpha scaling in expnorm and visualization function
Ruunyox Jul 18, 2021
c73ed5b
visualize-basis plt import
Ruunyox Jul 19, 2021
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30 changes: 29 additions & 1 deletion torchmdnet/models/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,33 @@
from torch_geometric.nn import radius_graph, MessagePassing


def visualize_basis(basis_type, num_rbf=50, cutoff_lower=0, cutoff_upper=5):
"""
Function for quickly visualizing a specific basis. This is useful for inspecting
the distance coverage of basis functions for non-default lower and upper cutoffs.

Args:
basis_type (str): Specifies the type of basis functions used. Can be one of
['gauss',expnorm']
num_rbf (int, optional): The number of basis functions.
(default: :obj:`50`)
cutoff_lower (float, optional): The lower cutoff of the basis.
(default: :obj:`0`)
cutoff_upper (float, optional): The upper cutoff of the basis.
(default: :obj:`5`)
"""
import matplotlib.pyplot as plt

distances = torch.linspace(cutoff_lower-1, cutoff_upper+1, 1000)
basis_kwargs = {'num_rbf':num_rbf, 'cutoff_lower':cutoff_lower, 'cutoff_upper':cutoff_upper}
basis_expansion = rbf_class_mapping[basis_type](**basis_kwargs)
expanded_distances = basis_expansion(distances)

for i in range(expanded_distances.shape[-1]):
plt.plot(distances.numpy(), expanded_distances[:,i].detach().numpy())
plt.show()


class NeighborEmbedding(MessagePassing):
def __init__(self, hidden_channels, num_rbf, cutoff_lower,
cutoff_upper, max_z=100):
Expand Down Expand Up @@ -83,6 +110,7 @@ def __init__(self, cutoff_lower=0.0, cutoff_upper=5.0, num_rbf=50, trainable=Tru
self.trainable = trainable

self.cutoff_fn = CosineCutoff(0, cutoff_upper)
self.alpha = 5.0/(cutoff_upper - cutoff_lower)

means, betas = self._initial_params()
if trainable:
Expand All @@ -107,7 +135,7 @@ def reset_parameters(self):

def forward(self, dist):
dist = dist.unsqueeze(-1)
return self.cutoff_fn(dist) * torch.exp(-self.betas * (torch.exp(-dist + self.cutoff_lower) - self.means) ** 2)
return self.cutoff_fn(dist) * torch.exp(-self.betas * (torch.exp(self.alpha*(-dist + self.cutoff_lower)) - self.means) ** 2)


class ShiftedSoftplus(nn.Module):
Expand Down