core.models.equiformer_v2.gaussian_rbf

core.models.equiformer_v2.gaussian_rbf#

Classes#

GaussianRadialBasisLayer

Base class for all neural network modules.

Functions#

gaussian(→ torch.Tensor)

Module Contents#

core.models.equiformer_v2.gaussian_rbf.gaussian(x: torch.Tensor, mean, std) → torch.Tensor#
class core.models.equiformer_v2.gaussian_rbf.GaussianRadialBasisLayer(num_basis: int, cutoff: float)#

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 to nest them 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):
        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 have their parameters converted too 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_basis#
cutoff#
mean#
std#
weight#
bias#
std_init_max = 1.0#
std_init_min#
mean_init_max = 1.0#
mean_init_min = 0#
forward(dist: torch.Tensor, node_atom=None, edge_src=None, edge_dst=None)#
extra_repr()#

Set the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

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