mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
acb73c729b
* - Remove spaces in `__repr__` of layers - Replace `size` by `kernel_size` in `__repr__` of a pooling layer * Fix flake8 errors
754 lines
21 KiB
Python
754 lines
21 KiB
Python
import warnings
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import torch
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from torch.nn.parameter import Parameter
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from .module import Module
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from .. import functional as F
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class Threshold(Module):
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r"""Thresholds each element of the input Tensor
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Threshold is defined as::
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y = x if x > threshold
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value if x <= threshold
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Args:
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threshold: The value to threshold at
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value: The value to replace with
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inplace: can optionally do the operation in-place. Default: False
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.Threshold(0.1, 20)
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>>> input = Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, threshold, value, inplace=False):
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super(Threshold, self).__init__()
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self.threshold = threshold
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self.value = value
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self.inplace = inplace
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# TODO: check in THNN (if inplace == True, then assert value <= threshold)
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def forward(self, input):
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return F.threshold(input, self.threshold, self.value, self.inplace)
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def __repr__(self):
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inplace_str = ', inplace' if self.inplace else ''
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return self.__class__.__name__ + ' (' \
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+ str(self.threshold) \
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+ ', ' + str(self.value) \
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+ inplace_str + ')'
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class ReLU(Threshold):
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r"""Applies the rectified linear unit function element-wise
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:math:`{ReLU}(x)= max(0, x)`
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Args:
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inplace: can optionally do the operation in-place. Default: False
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.ReLU()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, inplace=False):
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super(ReLU, self).__init__(0, 0, inplace)
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def __repr__(self):
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inplace_str = 'inplace' if self.inplace else ''
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return self.__class__.__name__ + '(' \
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+ inplace_str + ')'
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class RReLU(Module):
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def __init__(self, lower=1. / 8, upper=1. / 3, inplace=False):
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super(RReLU, self).__init__()
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self.lower = lower
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self.upper = upper
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self.inplace = inplace
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def forward(self, input):
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return F.rrelu(input, self.lower, self.upper, self.training, self.inplace)
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def __repr__(self):
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inplace_str = ', inplace' if self.inplace else ''
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return self.__class__.__name__ + '(' \
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+ str(self.lower) \
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+ ', ' + str(self.upper) \
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+ inplace_str + ')'
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class Hardtanh(Module):
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r"""Applies the HardTanh function element-wise
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HardTanh is defined as::
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f(x) = +1, if x > 1
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f(x) = -1, if x < -1
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f(x) = x, otherwise
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The range of the linear region :math:`[-1, 1]` can be adjusted
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Args:
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min_val: minimum value of the linear region range. Default: -1
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max_val: maximum value of the linear region range. Default: 1
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inplace: can optionally do the operation in-place. Default: False
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Keyword arguments :attr:`min_value` and :attr:`max_value`
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have been deprecated in favor of :attr:`min_val` and :attr:`max_val`
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.HardTanh(-2, 2)
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, min_val=-1, max_val=1, inplace=False, min_value=None, max_value=None):
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super(Hardtanh, self).__init__()
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if min_value is not None:
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warnings.warn("keyword argument min_value is deprecated and renamed to min_val")
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min_val = min_value
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if max_value is not None:
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warnings.warn("keyword argument max_value is deprecated and renamed to max_val")
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max_val = max_value
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self.min_val = min_val
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self.max_val = max_val
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self.inplace = inplace
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assert self.max_val > self.min_val
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def forward(self, input):
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return F.hardtanh(input, self.min_val, self.max_val, self.inplace)
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def __repr__(self):
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inplace_str = ', inplace' if self.inplace else ''
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return self.__class__.__name__ + '(' \
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+ 'min_val=' + str(self.min_val) \
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+ ', max_val=' + str(self.max_val) \
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+ inplace_str + ')'
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class ReLU6(Hardtanh):
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r"""Applies the element-wise function :math:`{ReLU6}(x) = min(max(0,x), 6)`
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Args:
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inplace: can optionally do the operation in-place. Default: False
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.ReLU6()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, inplace=False):
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super(ReLU6, self).__init__(0, 6, inplace)
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def __repr__(self):
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inplace_str = 'inplace' if self.inplace else ''
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return self.__class__.__name__ + '(' \
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+ inplace_str + ')'
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class Sigmoid(Module):
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r"""Applies the element-wise function :math:`f(x) = 1 / ( 1 + exp(-x))`
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.Sigmoid()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def forward(self, input):
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return torch.sigmoid(input)
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def __repr__(self):
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return self.__class__.__name__ + '()'
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class Tanh(Module):
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r"""Applies element-wise,
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:math:`f(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))`
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.Tanh()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def forward(self, input):
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return torch.tanh(input)
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def __repr__(self):
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return self.__class__.__name__ + '()'
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class ELU(Module):
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r"""Applies element-wise,
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:math:`f(x) = max(0,x) + min(0, alpha * (exp(x) - 1))`
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Args:
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alpha: the alpha value for the ELU formulation. Default: 1.0
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inplace: can optionally do the operation in-place. Default: False
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.ELU()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, alpha=1., inplace=False):
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super(ELU, self).__init__()
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self.alpha = alpha
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self.inplace = inplace
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def forward(self, input):
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return F.elu(input, self.alpha, self.inplace)
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def __repr__(self):
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inplace_str = ', inplace' if self.inplace else ''
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return self.__class__.__name__ + '(' \
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+ 'alpha=' + str(self.alpha) \
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+ inplace_str + ')'
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class SELU(Module):
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r"""Applies element-wise,
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:math:`f(x) = scale * (\max(0,x) + \min(0, alpha * (\exp(x) - 1)))`,
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with ``alpha=1.6732632423543772848170429916717`` and
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``scale=1.0507009873554804934193349852946``.
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More details can be found in the paper `Self-Normalizing Neural Networks`_ .
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Args:
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inplace (bool, optional): can optionally do the operation in-place. Default: False
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.SELU()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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.. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515
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"""
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def __init__(self, inplace=False):
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super(SELU, self).__init__()
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self.inplace = inplace
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def forward(self, input):
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return F.selu(input, self.inplace)
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def __repr__(self):
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inplace_str = '(inplace)' if self.inplace else ''
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return self.__class__.__name__ + inplace_str
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class GLU(Module):
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r"""Applies the gated linear unit function
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:math:`{GLU}(a, b)= a \otimes \sigma(b)` where `a` is the first half of
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the input vector and `b` is the second half.
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Args:
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dim (int): the dimension on which to split the input. Default: -1
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Shape:
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- Input: :math:`(*, N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(*, N / 2, *)`
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Examples::
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>>> m = nn.GLU()
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>>> input = autograd.Variable(torch.randn(4, 2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, dim=-1):
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super(GLU, self).__init__()
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self.dim = dim
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def forward(self, input):
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return F.glu(input, self.dim)
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def __repr__(self):
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return '{}(dim={})'.format(self.__class__.__name__, self.dim)
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class Hardshrink(Module):
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r"""Applies the hard shrinkage function element-wise
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Hardshrink is defined as::
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f(x) = x, if x > lambda
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f(x) = x, if x < -lambda
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f(x) = 0, otherwise
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Args:
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lambd: the lambda value for the Hardshrink formulation. Default: 0.5
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.Hardshrink()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, lambd=0.5):
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super(Hardshrink, self).__init__()
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self.lambd = lambd
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def forward(self, input):
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return F.hardshrink(input, self.lambd)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.lambd) + ')'
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class LeakyReLU(Module):
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r"""Applies element-wise,
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:math:`f(x) = max(0, x) + {negative\_slope} * min(0, x)`
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Args:
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negative_slope: Controls the angle of the negative slope. Default: 1e-2
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inplace: can optionally do the operation in-place. Default: False
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.LeakyReLU(0.1)
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, negative_slope=1e-2, inplace=False):
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super(LeakyReLU, self).__init__()
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self.negative_slope = negative_slope
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self.inplace = inplace
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def forward(self, input):
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return F.leaky_relu(input, self.negative_slope, self.inplace)
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def __repr__(self):
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inplace_str = ', inplace' if self.inplace else ''
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return self.__class__.__name__ + '(' \
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+ str(self.negative_slope) \
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+ inplace_str + ')'
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class LogSigmoid(Module):
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r"""Applies element-wise :math:`LogSigmoid(x) = log( 1 / (1 + exp(-x_i)))`
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.LogSigmoid()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def forward(self, input):
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return F.logsigmoid(input)
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def __repr__(self):
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return self.__class__.__name__ + '()'
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class Softplus(Module):
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r"""Applies element-wise :math:`f(x) = 1/beta * log(1 + exp(beta * x_i))`
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SoftPlus is a smooth approximation to the ReLU function and can be used
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to constrain the output of a machine to always be positive.
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For numerical stability the implementation reverts to the linear function
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for inputs above a certain value.
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Args:
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beta: the beta value for the Softplus formulation. Default: 1
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threshold: values above this revert to a linear function. Default: 20
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.Softplus()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, beta=1, threshold=20):
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super(Softplus, self).__init__()
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self.beta = beta
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self.threshold = threshold
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def forward(self, input):
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return F.softplus(input, self.beta, self.threshold)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ 'beta=' + str(self.beta) \
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+ ', threshold=' + str(self.threshold) + ')'
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class Softshrink(Module):
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r"""Applies the soft shrinkage function elementwise
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SoftShrinkage operator is defined as::
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f(x) = x-lambda, if x > lambda > f(x) = x+lambda, if x < -lambda
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f(x) = 0, otherwise
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Args:
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lambd: the lambda value for the Softshrink formulation. Default: 0.5
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.Softshrink()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, lambd=0.5):
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super(Softshrink, self).__init__()
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self.lambd = lambd
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def forward(self, input):
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return F.softshrink(input, self.lambd)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.lambd) + ')'
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class PReLU(Module):
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r"""Applies element-wise the function
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:math:`PReLU(x) = max(0,x) + a * min(0,x)` Here "a" is a learnable
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parameter. When called without arguments, nn.PReLU() uses a single
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parameter "a" across all input channels. If called with nn.PReLU(nChannels),
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a separate "a" is used for each input channel.
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.. note::
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weight decay should not be used when learning "a" for good performance.
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Args:
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num_parameters: number of "a" to learn. Default: 1
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init: the initial value of "a". Default: 0.25
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Shape:
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- Input: :math:`(N, *)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(N, *)`, same shape as the input
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Examples::
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>>> m = nn.PReLU()
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>>> input = autograd.Variable(torch.randn(2))
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>>> print(input)
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>>> print(m(input))
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"""
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def __init__(self, num_parameters=1, init=0.25):
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self.num_parameters = num_parameters
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super(PReLU, self).__init__()
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|
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) + ')'
|
|
|
|
|
|
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__ + '()'
|
|
|
|
|
|
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__ + '()'
|
|
|
|
|
|
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__ + '()'
|
|
|
|
|
|
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__ + '()'
|
|
|
|
|
|
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__ + '()'
|
|
|
|
|
|
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__ + '()'
|