# coding=utf-8
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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import oneflow as flow
import oneflow.nn as nn
[docs]def drop_path(x, drop_prob: float = 0.5, training: bool = False, scale_by_keep: bool = True):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
if drop_prob == 0.0 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
# similar opeartion to new_tensor(shape).bernoulli_(keep_prob)
random_tensor = flow.rand(*shape, dtype=x.dtype, sbp=x.sbp, placement=x.placement)
random_tensor = (random_tensor < keep_prob).to(flow.float32)
if keep_prob > 0.0 and scale_by_keep:
random_tensor = random_tensor / keep_prob
return x * random_tensor
[docs]class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)