Source code for super_gradients.training.utils.regularization_utils

import torch
from torch import nn


[docs]class DropPath(nn.Module): """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Code taken from TIMM (https://github.com/rwightman/pytorch-image-models) Apache License 2.0 """ def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob
[docs] def forward(self, x): if self.drop_prob == 0. or not self.training: return x keep_prob = 1 - self.drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output