core.models.gemnet_oc.layers.base_layers

core.models.gemnet_oc.layers.base_layers#

Copyright (c) Meta, Inc. and its affiliates. This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Classes#

Dense

Combines dense layer with scaling for silu activation.

ScaledSiLU

Base class for all neural network modules.

ResidualLayer

Residual block with output scaled by 1/sqrt(2).

Module Contents#

class core.models.gemnet_oc.layers.base_layers.Dense(in_features: int, out_features: int, bias: bool = False, activation: str | None = None)#

Bases: torch.nn.Module

Combines dense layer with scaling for silu activation.

Parameters:

  • in_features (int) – Input embedding size.

  • out_features (int) – Output embedding size.

  • bias (bool) – True if use bias.

  • activation (str) – Name of the activation function to use.

linear#
reset_parameters(initializer=he_orthogonal_init) → None#
forward(x)#
class core.models.gemnet_oc.layers.base_layers.ScaledSiLU#

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.

scale_factor#
_activation#
forward(x)#
class core.models.gemnet_oc.layers.base_layers.ResidualLayer(units: int, nLayers: int = 2, layer=Dense, **layer_kwargs)#

Bases: torch.nn.Module

Residual block with output scaled by 1/sqrt(2).

Parameters:

  • units (int) – Input and output embedding size.

  • nLayers (int) – Number of dense layers.

  • layer (torch.nn.Module) – Class for the layers inside the residual block.

  • layer_kwargs (str) – Keyword arguments for initializing the layers.

dense_mlp#
inv_sqrt_2#
forward(input)#
Contents