core.models.uma.nn.radial

core.models.uma.nn.radial#

Copyright (c) Meta Platforms, Inc. and affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Classes#

PolynomialEnvelope

Polynomial envelope function that ensures a smooth cutoff.

GaussianSmearing

Base class for all neural network modules.

RadialMLP

Contruct a radial function (linear layers + layer normalization + SiLU) given a list of channels

Functions#

gaussian(→ torch.Tensor)

Module Contents#

core.models.uma.nn.radial.gaussian(x: torch.Tensor, mean, std) → torch.Tensor#
class core.models.uma.nn.radial.PolynomialEnvelope(exponent: int = 5)#

Bases: torch.nn.Module

Polynomial envelope function that ensures a smooth cutoff.

p: float#
a: float#
b: float#
c: float#
forward(d_scaled: torch.Tensor) → torch.Tensor#
class core.models.uma.nn.radial.GaussianSmearing(start: float = -5.0, stop: float = 5.0, num_gaussians: int = 50, basis_width_scalar: float = 1.0)#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

num_output#
coeff#
forward(dist) → torch.Tensor#
class core.models.uma.nn.radial.RadialMLP(channels_list)#

Bases: torch.nn.Module

Contruct a radial function (linear layers + layer normalization + SiLU) given a list of channels

net#
forward(inputs: torch.Tensor) → torch.Tensor#
Contents