core.models.equiformer_v2.radial_function

core.models.equiformer_v2.radial_function#

Classes#

RadialFunction

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

Module Contents#

class core.models.equiformer_v2.radial_function.RadialFunction(channels_list)#

Bases: torch.nn.Module

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

modules = []#

Return an iterator over all modules in the network.

Yields:

Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
...     print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
input_channels#
net#
forward(inputs)#
Contents