import warnings
import torch
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
[docs]class Threshold(Module):
r"""Thresholds each element of the input Tensor
Threshold is defined as::
y = x if x > threshold
value if x <= threshold
Args:
threshold: The value to threshold at
value: The value to replace with
inplace: can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Threshold(0.1, 20)
>>> input = Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, threshold, value, inplace=False):
super(Threshold, self).__init__()
self.threshold = threshold
self.value = value
self.inplace = inplace
# TODO: check in THNN (if inplace == True, then assert value <= threshold)
def forward(self, input):
return F.threshold(input, self.threshold, self.value, self.inplace)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ str(self.threshold) \
+ ', ' + str(self.value) \
+ inplace_str + ')'
[docs]class ReLU(Threshold):
r"""Applies the rectified linear unit function element-wise
:math:`{ReLU}(x)= max(0, x)`
Args:
inplace: can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.ReLU()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, inplace=False):
super(ReLU, self).__init__(0, 0, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ inplace_str + ')'
class RReLU(Module):
def __init__(self, lower=1. / 8, upper=1. / 3, inplace=False):
super(RReLU, self).__init__()
self.lower = lower
self.upper = upper
self.inplace = inplace
def forward(self, input):
return F.rrelu(input, self.lower, self.upper, self.training, self.inplace)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ str(self.lower) \
+ ', ' + str(self.upper) \
+ inplace_str + ')'
[docs]class Hardtanh(Module):
r"""Applies the HardTanh function element-wise
HardTanh is defined as::
f(x) = +1, if x > 1
f(x) = -1, if x < -1
f(x) = x, otherwise
The range of the linear region :math:`[-1, 1]` can be adjusted
Args:
min_val: minimum value of the linear region range. Default: -1
max_val: maximum value of the linear region range. Default: 1
inplace: can optionally do the operation in-place. Default: ``False``
Keyword arguments :attr:`min_value` and :attr:`max_value`
have been deprecated in favor of :attr:`min_val` and :attr:`max_val`
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Hardtanh(-2, 2)
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, min_val=-1, max_val=1, inplace=False, min_value=None, max_value=None):
super(Hardtanh, self).__init__()
if min_value is not None:
warnings.warn("keyword argument min_value is deprecated and renamed to min_val")
min_val = min_value
if max_value is not None:
warnings.warn("keyword argument max_value is deprecated and renamed to max_val")
max_val = max_value
self.min_val = min_val
self.max_val = max_val
self.inplace = inplace
assert self.max_val > self.min_val
def forward(self, input):
return F.hardtanh(input, self.min_val, self.max_val, self.inplace)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ 'min_val=' + str(self.min_val) \
+ ', max_val=' + str(self.max_val) \
+ inplace_str + ')'
[docs]class ReLU6(Hardtanh):
r"""Applies the element-wise function :math:`{ReLU6}(x) = min(max(0,x), 6)`
Args:
inplace: can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.ReLU6()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, inplace=False):
super(ReLU6, self).__init__(0, 6, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ inplace_str + ')'
[docs]class Sigmoid(Module):
r"""Applies the element-wise function :math:`f(x) = 1 / ( 1 + exp(-x))`
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Sigmoid()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def forward(self, input):
return torch.sigmoid(input)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class Tanh(Module):
r"""Applies element-wise,
:math:`f(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))`
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Tanh()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def forward(self, input):
return torch.tanh(input)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class ELU(Module):
r"""Applies element-wise,
:math:`f(x) = max(0,x) + min(0, alpha * (exp(x) - 1))`
Args:
alpha: the alpha value for the ELU formulation. Default: 1.0
inplace: can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.ELU()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, alpha=1., inplace=False):
super(ELU, self).__init__()
self.alpha = alpha
self.inplace = inplace
def forward(self, input):
return F.elu(input, self.alpha, self.inplace)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ 'alpha=' + str(self.alpha) \
+ inplace_str + ')'
[docs]class SELU(Module):
r"""Applies element-wise,
:math:`f(x) = scale * (\max(0,x) + \min(0, alpha * (\exp(x) - 1)))`,
with ``alpha=1.6732632423543772848170429916717`` and
``scale=1.0507009873554804934193349852946``.
More details can be found in the paper `Self-Normalizing Neural Networks`_ .
Args:
inplace (bool, optional): can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.SELU()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
.. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
"""
def __init__(self, inplace=False):
super(SELU, self).__init__()
self.inplace = inplace
def forward(self, input):
return F.selu(input, self.inplace)
def __repr__(self):
inplace_str = '(inplace)' if self.inplace else ''
return self.__class__.__name__ + inplace_str
class GLU(Module):
r"""Applies the gated linear unit function
:math:`{GLU}(a, b)= a \otimes \sigma(b)` where `a` is the first half of
the input vector and `b` is the second half.
Args:
dim (int): the dimension on which to split the input. Default: -1
Shape:
- Input: :math:`(*, N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(*, N / 2, *)`
Examples::
>>> m = nn.GLU()
>>> input = autograd.Variable(torch.randn(4, 2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, dim=-1):
super(GLU, self).__init__()
self.dim = dim
def forward(self, input):
return F.glu(input, self.dim)
def __repr__(self):
return '{}(dim={})'.format(self.__class__.__name__, self.dim)
class Hardshrink(Module):
r"""Applies the hard shrinkage function element-wise
Hardshrink is defined as::
f(x) = x, if x > lambda
f(x) = x, if x < -lambda
f(x) = 0, otherwise
Args:
lambd: the lambda value for the Hardshrink formulation. Default: 0.5
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Hardshrink()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, lambd=0.5):
super(Hardshrink, self).__init__()
self.lambd = lambd
def forward(self, input):
return F.hardshrink(input, self.lambd)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.lambd) + ')'
[docs]class LeakyReLU(Module):
r"""Applies element-wise,
:math:`f(x) = max(0, x) + {negative\_slope} * min(0, x)`
Args:
negative_slope: Controls the angle of the negative slope. Default: 1e-2
inplace: can optionally do the operation in-place. Default: ``False``
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.LeakyReLU(0.1)
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, negative_slope=1e-2, inplace=False):
super(LeakyReLU, self).__init__()
self.negative_slope = negative_slope
self.inplace = inplace
def forward(self, input):
return F.leaky_relu(input, self.negative_slope, self.inplace)
def __repr__(self):
inplace_str = ', inplace' if self.inplace else ''
return self.__class__.__name__ + '(' \
+ str(self.negative_slope) \
+ inplace_str + ')'
[docs]class LogSigmoid(Module):
r"""Applies element-wise :math:`LogSigmoid(x) = log( 1 / (1 + exp(-x_i)))`
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.LogSigmoid()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def forward(self, input):
return F.logsigmoid(input)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class Softplus(Module):
r"""Applies element-wise :math:`f(x) = 1/beta * log(1 + exp(beta * x_i))`
SoftPlus is a smooth approximation to the ReLU function and can be used
to constrain the output of a machine to always be positive.
For numerical stability the implementation reverts to the linear function
for inputs above a certain value.
Args:
beta: the beta value for the Softplus formulation. Default: 1
threshold: values above this revert to a linear function. Default: 20
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Softplus()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, beta=1, threshold=20):
super(Softplus, self).__init__()
self.beta = beta
self.threshold = threshold
def forward(self, input):
return F.softplus(input, self.beta, self.threshold)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'beta=' + str(self.beta) \
+ ', threshold=' + str(self.threshold) + ')'
[docs]class Softshrink(Module):
r"""Applies the soft shrinkage function elementwise
SoftShrinkage operator is defined as::
f(x) = x-lambda, if x > lambda > f(x) = x+lambda, if x < -lambda
f(x) = 0, otherwise
Args:
lambd: the lambda value for the Softshrink formulation. Default: 0.5
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Softshrink()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, lambd=0.5):
super(Softshrink, self).__init__()
self.lambd = lambd
def forward(self, input):
return F.softshrink(input, self.lambd)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.lambd) + ')'
[docs]class PReLU(Module):
r"""Applies element-wise the function
:math:`PReLU(x) = max(0,x) + a * min(0,x)` Here "a" is a learnable
parameter. When called without arguments, nn.PReLU() uses a single
parameter "a" across all input channels. If called with nn.PReLU(nChannels),
a separate "a" is used for each input channel.
.. note::
weight decay should not be used when learning "a" for good performance.
Args:
num_parameters: number of "a" to learn. Default: 1
init: the initial value of "a". Default: 0.25
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.PReLU()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, num_parameters=1, init=0.25):
self.num_parameters = num_parameters
super(PReLU, self).__init__()
self.weight = Parameter(torch.Tensor(num_parameters).fill_(init))
def forward(self, input):
return F.prelu(input, self.weight)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'num_parameters=' + str(self.num_parameters) + ')'
[docs]class Softsign(Module):
r"""Applies element-wise, the function :math:`f(x) = x / (1 + |x|)`
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Softsign()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def forward(self, input):
return F.softsign(input)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class Tanhshrink(Module):
r"""Applies element-wise, :math:`Tanhshrink(x) = x - Tanh(x)`
Shape:
- Input: :math:`(N, *)` where `*` means, any number of additional
dimensions
- Output: :math:`(N, *)`, same shape as the input
Examples::
>>> m = nn.Tanhshrink()
>>> input = autograd.Variable(torch.randn(2))
>>> print(input)
>>> print(m(input))
"""
def forward(self, input):
return F.tanhshrink(input)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class Softmin(Module):
r"""Applies the Softmin function to an n-dimensional input Tensor
rescaling them so that the elements of the n-dimensional output Tensor
lie in the range `(0, 1)` and sum to 1
:math:`f(x) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}`
Shape:
- Input: any shape
- Output: same as input
Arguments:
dim (int): A dimension along which Softmax will be computed (so every slice
along dim will sum to 1).
Returns:
a Tensor of the same dimension and shape as the input, with
values in the range [0, 1]
Examples::
>>> m = nn.Softmin()
>>> input = autograd.Variable(torch.randn(2, 3))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, dim=None):
super(Softmin, self).__init__()
self.dim = dim
def forward(self, input):
return F.softmin(input, self.dim, _stacklevel=5)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class Softmax(Module):
r"""Applies the Softmax function to an n-dimensional input Tensor
rescaling them so that the elements of the n-dimensional output Tensor
lie in the range (0,1) and sum to 1
Softmax is defined as
:math:`f_i(x) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}`
Shape:
- Input: any shape
- Output: same as input
Returns:
a Tensor of the same dimension and shape as the input with
values in the range [0, 1]
Arguments:
dim (int): A dimension along which Softmax will be computed (so every slice
along dim will sum to 1).
.. note::
This module doesn't work directly with NLLLoss,
which expects the Log to be computed between the Softmax and itself.
Use Logsoftmax instead (it's faster and has better numerical properties).
Examples::
>>> m = nn.Softmax()
>>> input = autograd.Variable(torch.randn(2, 3))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, dim=None):
super(Softmax, self).__init__()
self.dim = dim
def __setstate__(self, state):
self.__dict__.update(state)
if not hasattr(self, 'dim'):
self.dim = None
def forward(self, input):
return F.softmax(input, self.dim, _stacklevel=5)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class Softmax2d(Module):
r"""Applies SoftMax over features to each spatial location
When given an image of Channels x Height x Width, it will
apply Softmax to each location :math:`(Channels, h_i, w_j)`
Shape:
- Input: :math:`(N, C, H, W)`
- Output: :math:`(N, C, H, W)` (same shape as input)
Returns:
a Tensor of the same dimension and shape as the input with
values in the range [0, 1]
Examples::
>>> m = nn.Softmax2d()
>>> # you softmax over the 2nd dimension
>>> input = autograd.Variable(torch.randn(2, 3, 12, 13))
>>> print(input)
>>> print(m(input))
"""
def forward(self, input):
assert input.dim() == 4, 'Softmax2d requires a 4D tensor as input'
return F.softmax(input, 1, _stacklevel=5)
def __repr__(self):
return self.__class__.__name__ + '()'
[docs]class LogSoftmax(Module):
r"""Applies the Log(Softmax(x)) function to an n-dimensional input Tensor.
The LogSoftmax formulation can be simplified as
:math:`f_i(x) = log(exp(x_i) / sum_j exp(x_j) )`
Shape:
- Input: any shape
- Output: same as input
Arguments:
dim (int): A dimension along which Softmax will be computed (so every slice
along dim will sum to 1).
Returns:
a Tensor of the same dimension and shape as the input with
values in the range [-inf, 0)
Examples::
>>> m = nn.LogSoftmax()
>>> input = autograd.Variable(torch.randn(2, 3))
>>> print(input)
>>> print(m(input))
"""
def __init__(self, dim=None):
super(LogSoftmax, self).__init__()
self.dim = dim
def __setstate__(self, state):
self.__dict__.update(state)
if not hasattr(self, 'dim'):
self.dim = None
def forward(self, input):
return F.log_softmax(input, self.dim, _stacklevel=5)
def __repr__(self):
return self.__class__.__name__ + '()'