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hub / github.com/CDTrans/CDTrans / drop_path

Function drop_path

model/backbones/vit_pytorch_uda.py:45–62  ·  view source on GitHub ↗

Drop paths (Stochastic Depth) per sample (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... Se

(x, drop_prob: float = 0., training: bool = False)

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Callers 1

forwardMethod · 0.70

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