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import torch.nn as nn
from itertools import repeat
from typing import Iterable
def _ntuple(n):
"""Copy item to be a tuple with n length (Implemented as timm)
"""
def parse(x):
if isinstance(x, Iterable):
return x
else:
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
to_ntuple = _ntuple
class DropPath(nn.Module):
"""Stochasticly zero channels of data.(Implemented as timm)
"""
def __init__(self, drop=0.5, scale=True):
super().__init__()
self.drop = drop
self.scale = scale
def forward(self, x):
return self.drop_path(x, self.drop, self.training, self.scale)
def drop_path(self, x, drop=0.5, training=True, scale=True):
if drop == 0. or not training:
return x
drop_p = 1 - drop
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = x.new_empty(shape).bernoulli_(drop_p)
if drop_p > 0. and scale:
random_tensor.div_(drop_p)
return x * random_tensor |