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102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
from .module import Module
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class Dropout(Module):
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"""Randomly zeroes some of the elements of the input tensor.
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The elements to zero are randomized on every forward call.
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Args:
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p: probability of an element to be zeroed. Default: 0.5
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inplace: If set to True, will do this operation in-place. Default: false
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Input Shape: Any : Input can be of any shape
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Output Shape:Same : Output is of the same shape as input
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Examples:
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>>> m = nn.Dropout(p=0.2)
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>>> input = autograd.Variable(torch.randn(20, 16))
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>>> output = m(input)
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"""
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def __init__(self, p=0.5, inplace=False):
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super(Dropout, self).__init__()
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self.p = p
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self.inplace = inplace
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def forward(self, input):
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return self._backend.Dropout(self.p, self.training, self.inplace)(input)
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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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+ 'p = ' + str(self.p) \
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+ inplace_str + ')'
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class Dropout2d(Module):
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"""Randomly zeroes whole channels of the input tensor.
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The input is 4D (batch x channels, height, width) and each channel
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is of size (1, height, width).
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The channels to zero are randomized on every forward call.
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Usually the input comes from Conv2d modules.
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As described in the paper "Efficient Object Localization Using Convolutional
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Networks" (http:arxiv.org/abs/1411.4280), if adjacent pixels within
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feature maps are strongly correlated (as is normally the case in early
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convolution layers) then iid dropout will not regularize the activations
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and will otherwise just result in an effective learning rate decrease.
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In this case, nn.Dropout2d will help promote independence between
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feature maps and should be used instead.
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Args:
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p: probability of an element to be zeroed. Default: 0.5
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inplace: If set to True, will do this operation in-place. Default: false
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Input Shape: [*, *, *, *] : Input can be of any sizes of 4D shape
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Output Shape:Same : Output is of the same shape as input
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Examples:
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>>> m = nn.Dropout2d(p=0.2)
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>>> input = autograd.Variable(torch.randn(20, 16, 32, 32))
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>>> output = m(input)
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"""
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def __init__(self, p=0.5, inplace=False):
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super(Dropout2d, self).__init__()
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self.p = p
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self.inplace = inplace
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def forward(self, input):
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return self._backend.Dropout2d(self.p, self.training, self.inplace)(input)
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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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+ 'p=' + str(self.p) \
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+ inplace_str + ')'
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class Dropout3d(Module):
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"""Randomly zeroes whole channels of the input tensor.
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The input is 5D (batch x channels, depth, height, width) and each channel
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is of size (1, depth, height, width).
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The channels to zero are randomized on every forward call.
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Usually the input comes from Conv3d modules.
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Args:
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p: probability of an element to be zeroed. Default: 0.5
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inplace: If set to True, will do this operation in-place. Default: false
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Input Shape: [*, *, *, *, *] : Input can be of any sizes of 5D shape
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Output Shape:Same : Output is of the same shape as input
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Examples:
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>>> m = nn.Dropout3d(p=0.2)
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>>> input = autograd.Variable(torch.randn(20, 16, 4, 32, 32))
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>>> output = m(input)
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"""
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def __init__(self, p=0.5, inplace=False):
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super(Dropout3d, self).__init__()
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self.p = p
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self.inplace = inplace
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def forward(self, input):
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return self._backend.Dropout3d(self.p, self.training, self.inplace)(input)
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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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+ 'p=' + str(self.p) \
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+ inplace_str + ')'
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