[D] Timm's implementation of stochastic depth

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  • pytorch-image-models

    PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

    • In Timm's implementation of stochastic depth (https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/layers/drop.py) the tensor is scaled by the probability of keeping the actual block. I didn't understand why he does so specially that this is not mentioned in the paper.

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