braindecode.functional.drop_path#

braindecode.functional.drop_path(x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True)[source]#

Drop paths (Stochastic Depth) per sample.

Notes: This implementation is taken from timm library.

All credit goes to Ross Wightman.

Parameters:

  • x (torch.Tensor) – input tensor

  • drop_prob (float, optional) – survival rate (i.e. probability of being kept), by default 0.0

  • training (bool, optional) – whether the model is in training mode, by default False

  • scale_by_keep (bool, optional) – whether to scale output by (1/keep_prob) during training, by default True

Returns:

  • torch.Tensor – output tensor

  • Notes from Ross Wightman

  • (when applied in main path of residual blocks)

  • This is the same as the DropConnect impl I created for EfficientNet,

  • etc. networks, however,

  • the original name is misleading as ‘Drop Connect’ is a different form

  • of dropout in a separate paper…

  • See discussion (https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956)

  • … I’ve opted for changing the layer and argument names to ‘drop path’

  • rather than mix DropConnect as a layer name and use

  • ’survival rate’ as the argument.

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