class torchvision.ops.SqueezeExcitation(input_channels: int, squeeze_channels: int, activation: ~typing.Callable[[...], ~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.ReLU'>, scale_activation: ~typing.Callable[[...], ~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.Sigmoid'>)[source]

This block implements the Squeeze-and-Excitation block from (see Fig. 1). Parameters activation, and scale_activation correspond to delta and sigma in eq. 3.

  • input_channels (int) – Number of channels in the input image

  • squeeze_channels (int) – Number of squeeze channels

  • activation (Callable[..., torch.nn.Module], optional) – delta activation. Default: torch.nn.ReLU

  • scale_activation (Callable[..., torch.nn.Module]) – sigma activation. Default: torch.nn.Sigmoid

forward(input: Tensor) Tensor[source]

Define the computation performed at every call.

Should be overridden by all subclasses.


Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.


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