core.models.equiformer_v2.radial_function#
Classes#
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)#