def __init__(self, in_features, activation_fn="Sigmoid"):
"""@TODO: Docs. Contribution is welcome."""
super().__init__()
activation_fn = REGISTRY.get_if_str(activation_fn)
self.attn = nn.Sequential(
nn.Conv2d(in_features, 1, kernel_size=1, stride=1, padding=0, bias=False),
activation_fn(),
)
def _activation_fn(activation_fn, f_in, f_out, **kwargs):
activation_fn = REGISTRY.get_if_str(activation_fn)
activation_fn = activation_fn(
**kwargs) if activation_fn is not None else None
return activation_fn
def _normalization_fn(normalization_fn, f_in, f_out, **kwargs):
normalization_fn = REGISTRY.get_if_str(normalization_fn)
normalization_fn = normalization_fn(
f_out, **kwargs) if normalization_fn is not None else None
return normalization_fn
def _dropout_fn(dropout_fn, f_in, f_out, **kwargs):
dropout_fn = REGISTRY.get_if_str(dropout_fn)
dropout_fn = dropout_fn(**kwargs) if dropout_fn is not None else None
return dropout_fn
def _layer_fn(layer_fn, f_in, f_out, **kwargs):
layer_fn = REGISTRY.get_if_str(layer_fn)
layer_fn = layer_fn(f_in, f_out, **kwargs)
return layer_fn