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modules.py
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import paddle
import paddle.nn as nn
class Identity(nn.Layer):
def __init__(self):
super(Identity, self).__init__()
def forward(self, input):
return input
def drop_path(x, drop_prob=0., training=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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 ...
"""
if drop_prob == 0. or not training:
return x
keep_prob = paddle.to_tensor(1 - drop_prob)
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
random_tensor = paddle.floor(random_tensor) # binarize
output = x.divide(keep_prob) * random_tensor
return output
class DropPath(nn.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
# x[int] -> tuple(x, x)
def to_2tuple(x):
return tuple([x] * 2)