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from .module import Module
from .. import functional as F
from torch import Tensor
class _DropoutNd(Module):
__constants__ = ['p', 'inplace']
p: float
inplace: bool
def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
super(_DropoutNd, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got {}".format(p))
self.p = p
self.inplace = inplace
def extra_repr(self) -> str:
return 'p={}, inplace={}'.format(self.p, self.inplace)
class Dropout(_DropoutNd):
r"""During training, randomly zeroes some of the elements of the input
tensor with probability :attr:`p` using samples from a Bernoulli
distribution. Each channel will be zeroed out independently on every forward
call.
This has proven to be an effective technique for regularization and
preventing the co-adaptation of neurons as described in the paper
`Improving neural networks by preventing co-adaptation of feature
detectors`_ .
Furthermore, the outputs are scaled by a factor of :math:`\frac{1}{1-p}` during
training. This means that during evaluation the module simply computes an
identity function.
Args:
p: probability of an element to be zeroed. Default: 0.5
inplace: If set to ``True``, will do this operation in-place. Default: ``False``
Shape:
- Input: :math:`(*)`. Input can be of any shape
- Output: :math:`(*)`. Output is of the same shape as input
Examples::
>>> m = nn.Dropout(p=0.2)
>>> input = torch.randn(20, 16)
>>> output = m(input)
.. _Improving neural networks by preventing co-adaptation of feature
detectors: https://arxiv.org/abs/1207.0580
"""
def forward(self, input: Tensor) -> Tensor:
return F.dropout(input, self.p, self.training, self.inplace)
class Dropout2d(_DropoutNd):
r"""Randomly zero out entire channels (a channel is a 2D feature map,
e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
batched input is a 2D tensor :math:`\text{input}[i, j]`).
Each channel will be zeroed out independently on every forward call with
probability :attr:`p` using samples from a Bernoulli distribution.
Usually the input comes from :class:`nn.Conv2d` modules.
As described in the paper
`Efficient Object Localization Using Convolutional Networks`_ ,
if adjacent pixels within feature maps are strongly correlated
(as is normally the case in early convolution layers) then i.i.d. dropout
will not regularize the activations and will otherwise just result
in an effective learning rate decrease.
In this case, :func:`nn.Dropout2d` will help promote independence between
feature maps and should be used instead.
Args:
p (float, optional): probability of an element to be zero-ed.
inplace (bool, optional): If set to ``True``, will do this operation
in-place
Shape:
- Input: :math:`(N, C, H, W)`
- Output: :math:`(N, C, H, W)` (same shape as input)
Examples::
>>> m = nn.Dropout2d(p=0.2)
>>> input = torch.randn(20, 16, 32, 32)
>>> output = m(input)
.. _Efficient Object Localization Using Convolutional Networks:
https://arxiv.org/abs/1411.4280
"""
def forward(self, input: Tensor) -> Tensor:
return F.dropout2d(input, self.p, self.training, self.inplace)
class Dropout3d(_DropoutNd):
r"""Randomly zero out entire channels (a channel is a 3D feature map,
e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
batched input is a 3D tensor :math:`\text{input}[i, j]`).
Each channel will be zeroed out independently on every forward call with
probability :attr:`p` using samples from a Bernoulli distribution.
Usually the input comes from :class:`nn.Conv3d` modules.
As described in the paper
`Efficient Object Localization Using Convolutional Networks`_ ,
if adjacent pixels within feature maps are strongly correlated
(as is normally the case in early convolution layers) then i.i.d. dropout
will not regularize the activations and will otherwise just result
in an effective learning rate decrease.
In this case, :func:`nn.Dropout3d` will help promote independence between
feature maps and should be used instead.
Args:
p (float, optional): probability of an element to be zeroed.
inplace (bool, optional): If set to ``True``, will do this operation
in-place
Shape:
- Input: :math:`(N, C, D, H, W)`
- Output: :math:`(N, C, D, H, W)` (same shape as input)
Examples::
>>> m = nn.Dropout3d(p=0.2)
>>> input = torch.randn(20, 16, 4, 32, 32)
>>> output = m(input)
.. _Efficient Object Localization Using Convolutional Networks:
https://arxiv.org/abs/1411.4280
"""
def forward(self, input: Tensor) -> Tensor:
return F.dropout3d(input, self.p, self.training, self.inplace)
class AlphaDropout(_DropoutNd):
r"""Applies Alpha Dropout over the input.
Alpha Dropout is a type of Dropout that maintains the self-normalizing
property.
For an input with zero mean and unit standard deviation, the output of
Alpha Dropout maintains the original mean and standard deviation of the
input.
Alpha Dropout goes hand-in-hand with SELU activation function, which ensures
that the outputs have zero mean and unit standard deviation.
During training, it randomly masks some of the elements of the input
tensor with probability *p* using samples from a bernoulli distribution.
The elements to masked are randomized on every forward call, and scaled
and shifted to maintain zero mean and unit standard deviation.
During evaluation the module simply computes an identity function.
More details can be found in the paper `Self-Normalizing Neural Networks`_ .
Args:
p (float): probability of an element to be dropped. Default: 0.5
inplace (bool, optional): If set to ``True``, will do this operation
in-place
Shape:
- Input: :math:`(*)`. Input can be of any shape
- Output: :math:`(*)`. Output is of the same shape as input
Examples::
>>> m = nn.AlphaDropout(p=0.2)
>>> input = torch.randn(20, 16)
>>> output = m(input)
.. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
"""
def forward(self, input: Tensor) -> Tensor:
return F.alpha_dropout(input, self.p, self.training)
class FeatureAlphaDropout(_DropoutNd):
r"""Randomly masks out entire channels (a channel is a feature map,
e.g. the :math:`j`-th channel of the :math:`i`-th sample in the batch input
is a tensor :math:`\text{input}[i, j]`) of the input tensor). Instead of
setting activations to zero, as in regular Dropout, the activations are set
to the negative saturation value of the SELU activation function. More details
can be found in the paper `Self-Normalizing Neural Networks`_ .
Each element will be masked independently for each sample on every forward
call with probability :attr:`p` using samples from a Bernoulli distribution.
The elements to be masked are randomized on every forward call, and scaled
and shifted to maintain zero mean and unit variance.
Usually the input comes from :class:`nn.AlphaDropout` modules.
As described in the paper
`Efficient Object Localization Using Convolutional Networks`_ ,
if adjacent pixels within feature maps are strongly correlated
(as is normally the case in early convolution layers) then i.i.d. dropout
will not regularize the activations and will otherwise just result
in an effective learning rate decrease.
In this case, :func:`nn.AlphaDropout` will help promote independence between
feature maps and should be used instead.
Args:
p (float, optional): probability of an element to be zeroed. Default: 0.5
inplace (bool, optional): If set to ``True``, will do this operation
in-place
Shape:
- Input: :math:`(N, C, D, H, W)`
- Output: :math:`(N, C, D, H, W)` (same shape as input)
Examples::
>>> m = nn.FeatureAlphaDropout(p=0.2)
>>> input = torch.randn(20, 16, 4, 32, 32)
>>> output = m(input)
.. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
.. _Efficient Object Localization Using Convolutional Networks:
https://arxiv.org/abs/1411.4280
"""
def forward(self, input: Tensor) -> Tensor:
return F.feature_alpha_dropout(input, self.p, self.training)
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