295 lines
11 KiB
Python
295 lines
11 KiB
Python
from .module import Module
|
|
from .. import functional as F
|
|
|
|
from torch import Tensor
|
|
|
|
__all__ = ['Dropout', 'Dropout1d', 'Dropout2d', 'Dropout3d', 'AlphaDropout', 'FeatureAlphaDropout']
|
|
|
|
class _DropoutNd(Module):
|
|
__constants__ = ['p', 'inplace']
|
|
p: float
|
|
inplace: bool
|
|
|
|
def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
|
|
super().__init__()
|
|
if p < 0 or p > 1:
|
|
raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
|
|
self.p = p
|
|
self.inplace = inplace
|
|
|
|
def extra_repr(self) -> str:
|
|
return f'p={self.p}, inplace={self.inplace}'
|
|
|
|
|
|
class Dropout(_DropoutNd):
|
|
r"""During training, randomly zeroes some of the elements of the input tensor with probability :attr:`p`.
|
|
|
|
The zeroed elements are chosen independently for each forward call and are sampled 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 Dropout1d(_DropoutNd):
|
|
r"""Randomly zero out entire channels.
|
|
|
|
A channel is a 1D feature map,
|
|
e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
|
|
batched input is a 1D 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.Conv1d` 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.Dropout1d` 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, L)` or :math:`(C, L)`.
|
|
- Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input).
|
|
|
|
Examples::
|
|
|
|
>>> m = nn.Dropout1d(p=0.2)
|
|
>>> input = torch.randn(20, 16, 32)
|
|
>>> output = m(input)
|
|
|
|
.. _Efficient Object Localization Using Convolutional Networks:
|
|
https://arxiv.org/abs/1411.4280
|
|
"""
|
|
|
|
def forward(self, input: Tensor) -> Tensor:
|
|
return F.dropout1d(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
|
|
|
|
.. warning ::
|
|
Due to historical reasons, this class will perform 1D channel-wise dropout
|
|
for 3D inputs (as done by :class:`nn.Dropout1d`). Thus, it currently does NOT
|
|
support inputs without a batch dimension of shape :math:`(C, H, W)`. This
|
|
behavior will change in a future release to interpret 3D inputs as no-batch-dim
|
|
inputs. To maintain the old behavior, switch to :class:`nn.Dropout1d`.
|
|
|
|
Shape:
|
|
- Input: :math:`(N, C, H, W)` or :math:`(N, C, L)`.
|
|
- Output: :math:`(N, C, H, W)` or :math:`(N, C, L)` (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)` or :math:`(C, D, H, W)`.
|
|
- Output: :math:`(N, C, D, H, W)` or :math:`(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)` or :math:`(C, D, H, W)`.
|
|
- Output: :math:`(N, C, D, H, W)` or :math:`(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)
|