MLP

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MLP#

class stable_ssl.backbone.MLP(in_channels: int, hidden_channels: list[int], norm_layer: str | None = None, activation_layer=<class 'torch.nn.modules.activation.ReLU'>, inplace: bool | None = None, bias: bool = True, dropout: float = 0.0)[source]#

Bases: Sequential

This block implements the multi-layer perceptron (MLP) module.

Parameters:

  • in_channels (int) – Number of channels of the input

  • hidden_channels (List[int]) – List of the hidden channel dimensions

  • norm_layer (Callable[..., torch.nn.Module], optional) – Norm layer that will be stacked on top of the linear layer. If None this layer won’t be used. Default: None

  • activation_layer (Callable[..., torch.nn.Module], optional) – Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the linear layer. If None this layer won’t be used. Default: torch.nn.ReLU

  • inplace (bool, optional) – Parameter for the activation layer, which can optionally do the operation in-place. Default is None, which uses the respective default values of the activation_layer and Dropout layer.

  • bias (bool) – Whether to use bias in the linear layer. Default True

  • dropout (float) – The probability for the dropout layer. Default: 0.0

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