# Copyright 2021 Ross Wightman . All rights reserved.
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# This code is modified by Zilliz.
""" DropPath
PyTorch implementations of DropPath (Stochastic Depth) regularization layers.
Papers:
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Copyright 2020 Ross Wightman
"""
import torch
from torch import nn
[docs]def drop_path(x, drop_prob: float = 0., training: bool = False):
"""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...
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.
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - 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
[docs]class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
[docs] def __init__(self, drop_prob=None):
super().__init__()
self.drop_prob = drop_prob
[docs] def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
