mirror of
https://github.com/zebrajr/pytorch.git
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747 lines
28 KiB
Python
747 lines
28 KiB
Python
"""Functional interface"""
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from numbers import Integral
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import torch
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from . import _functions
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from .modules import utils
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from ._functions.padding import ConstantPad2d
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from ..autograd import _functions as _autograd_functions
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from .modules.utils import _single, _pair, _triple
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# Convolutions
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ConvNd = torch._C._functions.ConvNd
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def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
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groups=1):
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"""Applies a 2D convolution over an input image composed of several input
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planes.
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See :class:`~torch.nn.Conv2d` for details and output shape.
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Args:
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input: input tensor (minibatch x in_channels x iH x iW)
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weight: filters tensor (out_channels, in_channels/groups, kH, kW)
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bias: optional bias tensor (out_channels)
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stride: the stride of the convolving kernel. Can be a single number or
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a tuple (sh x sw). Default: 1
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padding: implicit zero padding on the input. Can be a single number or
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a tuple. Default: 0
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dilation: the spacing between kernel elements. Default: 1
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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Examples:
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>>> # With square kernels and equal stride
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>>> filters = autograd.Variable(torch.randn(8,4,3,3))
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>>> inputs = autograd.Variable(torch.randn(1,4,5,5))
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>>> F.conv2d(inputs, filters, padding=1)
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"""
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f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), False,
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_pair(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
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return f(input, weight, bias)
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def conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1,
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groups=1):
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"""Applies a 1D convolution over an input signal composed of several input
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planes.
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See :class:`~torch.nn.Conv1d` for details and output shape.
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Args:
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input: input tensor of shape (minibatch x in_channels x iW)
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weight: filters of shape (out_channels, in_channels, kW)
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bias: optional bias of shape (out_channels)
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stride: the stride of the convolving kernel, default 1
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padding: implicit zero padding on the input. Can be a single number or
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a tuple. Default: 0
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dilation: the spacing between kernel elements. Default: 1
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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Examples:
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>>> filters = autograd.Variable(torch.randn(33, 16, 3))
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>>> inputs = autograd.Variable(torch.randn(20, 16, 50))
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>>> F.conv1d(inputs, filters)
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"""
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f = ConvNd(_single(stride), _single(padding), _single(dilation), False,
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_single(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
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return f(input, weight, bias)
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def conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1,
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groups=1):
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"""Applies a 3D convolution over an input image composed of several input
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planes.
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See :class:`~torch.nn.Conv3d` for details and output shape.
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Args:
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input: input tensor of shape (minibatch x in_channels x iT x iH x iW)
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weight: filters tensor of shape (out_channels, in_channels, kT, kH, kW)
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bias: optional bias tensor of shape (out_channels)
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stride: the stride of the convolving kernel. Can be a single number or
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a tuple (st x sh x sw). Default: 1
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padding: implicit zero padding on the input. Can be a single number or
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a tuple. Default: 0
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dilation: the spacing between kernel elements. Default: 1
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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Examples:
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>>> filters = autograd.Variable(torch.randn(33, 16, 3, 3, 3))
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>>> inputs = autograd.Variable(torch.randn(20, 16, 50, 10, 20))
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>>> F.conv3d(inputs, filters)
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"""
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f = ConvNd(_triple(stride), _triple(padding), _triple(dilation), False,
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_triple(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
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return f(input, weight, bias)
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def conv_transpose1d(input, weight, bias=None, stride=1, padding=0,
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output_padding=0, groups=1, dilation=1):
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f = ConvNd(_single(stride), _single(padding), _single(dilation), True,
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_single(output_padding),
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groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
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return f(input, weight, bias)
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def conv_transpose2d(input, weight, bias=None, stride=1, padding=0,
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output_padding=0, groups=1, dilation=1):
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"""Applies a 2D transposed convolution operator over an input image
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composed of several input planes, sometimes also called "deconvolution".
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See :class:`~torch.nn.ConvTranspose2d` for details and output shape.
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Args:
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input: input tensor of shape (minibatch x in_channels x iH x iW)
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weight: filters of shape (in_channels x out_channels x kH x kW)
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bias: optional bias of shape (out_channels)
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stride: the stride of the convolving kernel, a single number or a
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tuple (sh x sw). Default: 1
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padding: implicit zero padding on the input, a single number or a
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tuple (padh x padw). Default: 0
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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output_padding: A zero-padding of 0 <= padding < stride that should be
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added to the output. Can be a single number or a tuple. Default: 0
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dilation: the spacing between kernel elements. Default: 1
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"""
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f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), True,
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_pair(output_padding), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
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return f(input, weight, bias)
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def conv_transpose3d(input, weight, bias=None, stride=1, padding=0,
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output_padding=0, groups=1, dilation=1):
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"""Applies a 3D transposed convolution operator over an input image
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composed of several input planes, sometimes also called "deconvolution"
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See :class:`~torch.nn.ConvTranspose3d` for details and output shape.
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Args:
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input: input tensor of shape (minibatch x in_channels x iT x iH x iW)
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weight: filters of shape (in_channels x out_channels x kH x kW)
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bias: optional bias of shape (out_channels)
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stride: the stride of the convolving kernel, a single number or a
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tuple (sh x sw). Default: 1
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padding: implicit zero padding on the input, a single number or a
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tuple (padh x padw). Default: 0
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output_padding: A zero-padding of 0 <= padding < stride that should be
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added to the output. Can be a single number or a tuple. Default: 0
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groups: split input into groups, in_channels should be divisible by
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the number of groups
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dilation: the spacing between kernel elements. Default: 1
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"""
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f = ConvNd(_triple(stride), _triple(padding), _triple(dilation), True,
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_triple(output_padding), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.enabled)
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return f(input, weight, bias)
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# Pooling
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def avg_pool1d(input, kernel_size, stride=None, padding=0,
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ceil_mode=False, count_include_pad=True):
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r"""Applies a 1D average pooling over an input signal composed of several
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input planes.
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See :class:`~torch.nn.AvgPool1d` for details and output shape.
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Args:
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kernel_size: the size of the window
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stride: the stride of the window. Default value is :attr:`kernel_size`
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padding: implicit zero padding to be added on both sides
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ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape
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count_include_pad: when True, will include the zero-padding in the averaging calculation
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Example:
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>>> # pool of square window of size=3, stride=2
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>>> input = Variable(torch.Tensor([[[1,2,3,4,5,6,7]]]))
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>>> F.avg_pool1d(input, kernel_size=3, stride=2)
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Variable containing:
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(0 ,.,.) =
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2 4 6
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[torch.FloatTensor of size 1x1x3]
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"""
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if input.dim() != 3:
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raise ValueError('expected 3D input (got {} dimensions)'
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.format(input.dim()))
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kernel_size = _single(kernel_size) + (1,)
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stride = _single(stride) + (1,) if stride is not None else kernel_size
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padding = _single(padding) + (0,)
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f = _functions.thnn.AvgPool2d(kernel_size, stride, padding,
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ceil_mode, count_include_pad)
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return f(input.unsqueeze(3)).squeeze(3)
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def avg_pool2d(input, kernel_size, stride=None, padding=0,
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ceil_mode=False, count_include_pad=True):
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"""Applies 2D average-pooling operation in kh x kw regions by step size
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dh x dw steps. The number of output features is equal to the number of
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input planes.
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See :class:`~torch.nn.AvgPool2d` for details and output shape.
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Args:
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input: input tensor (minibatch x in_channels x iH x iW)
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kernel_size: size of the pooling region, a single number or a
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tuple (kh x kw)
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stride: stride of the pooling operation, a single number or a
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tuple (sh x sw). Default is equal to kernel size
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padding: implicit zero padding on the input, a single number or
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a tuple (padh x padw), Default: 0
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ceil_mode: when True, will use `ceil` instead of `floor` in the formula to compute the output shape
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count_include_pad: when True, will include the zero-padding in the averaging calculation
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"""
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return _functions.thnn.AvgPool2d(kernel_size, stride, padding,
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ceil_mode, count_include_pad)(input)
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def avg_pool3d(input, kernel_size, stride=None):
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"""Applies 3D average-pooling operation in kt x kh x kw regions by step
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size dt x dh x dw steps. The number of output features is equal to the
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number of input planes / dt.
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"""
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return _functions.thnn.AvgPool3d(kernel_size, stride)(input)
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# share the same interface
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def max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1,
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ceil_mode=False, return_indices=False):
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return _functions.thnn.MaxPool1d(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)(input)
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def max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1,
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ceil_mode=False, return_indices=False):
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return _functions.thnn.MaxPool2d(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)(input)
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def max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1,
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ceil_mode=False, return_indices=False):
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return _functions.thnn.MaxPool3d(kernel_size, stride, padding, dilation,
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return_indices, ceil_mode)(input)
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def _unpool_output_size(input, kernel_size, stride, padding, output_size):
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input_size = input.size()
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default_size = []
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for d in range(len(kernel_size)):
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default_size.append((input_size[d + 2] - 1) * stride[d] +
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kernel_size[d] - 2 * padding[d])
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if output_size is None:
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return default_size
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output_size = list(output_size)
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if len(output_size) == len(kernel_size) + 2:
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output_size = output_size[2:]
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if len(output_size) != len(kernel_size):
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raise ValueError("output_size should be a sequence containing "
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"{} or {} elements, but it has a length of '{}'"
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.format(len(kernel_size), len(kernel_size) + 2,
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len(output_size)))
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for d in range(len(kernel_size)):
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min_size = default_size[d] - stride[d]
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max_size = default_size[d] + stride[d]
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if not (min_size < output_size[d] < max_size):
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raise ValueError(
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'invalid output_size "{}" (dim {} must be between {} and {})'
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.format(output_size, d, min_size, max_size))
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return output_size
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def max_unpool1d(input, indices, kernel_size, stride=None, padding=0,
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output_size=None):
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kernel_size = _single(kernel_size)
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stride = _single(stride)
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padding = _single(padding)
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output_size = _unpool_output_size(input, kernel_size, stride, padding,
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output_size)
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f = _functions.thnn.MaxUnpool2d(output_size + [1])
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return f(input.unsqueeze(3), indices.unsqueeze(3)).squeeze(3)
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def max_unpool2d(input, indices, kernel_size, stride=None, padding=0,
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output_size=None):
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kernel_size = _pair(kernel_size)
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stride = _pair(stride)
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padding = _pair(padding)
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output_size = _unpool_output_size(input, kernel_size, stride, padding,
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output_size)
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f = _functions.thnn.MaxUnpool2d(output_size)
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return f(input, indices)
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def max_unpool3d(input, indices, kernel_size, stride=None, padding=0,
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output_size=None):
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kernel_size = _triple(kernel_size)
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stride = _triple(stride)
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padding = _triple(padding)
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output_size = _unpool_output_size(input, kernel_size, stride, padding,
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output_size)
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f = _functions.thnn.MaxUnpool3d(output_size, stride, padding)
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return f(input, indices)
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def lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False):
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kw, kh = utils._pair(kernel_size)
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out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
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return out.mul(kw * kh).pow(1. / norm_type)
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def adaptive_max_pool1d(input, output_size, return_indices=False):
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r"""Applies a 1D adaptive max pooling over an input signal composed of
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several input planes.
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See :class:`~torch.nn.AdaptiveMaxPool1d` for details and output shape.
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Args:
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output_size: the target output size (single integer)
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return_indices: whether to return pooling indices
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"""
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return _functions.thnn.AdaptiveMaxPool1d(output_size, return_indices)(input)
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def adaptive_max_pool2d(input, output_size, return_indices=False):
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r"""Applies a 2D adaptive max pooling over an input signal composed of
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several input planes.
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See :class:`~torch.nn.AdaptiveMaxPool2d` for details and output shape.
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Args:
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output_size: the target output size (single integer or double-integer tuple)
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return_indices: whether to return pooling indices
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"""
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return _functions.thnn.AdaptiveMaxPool2d(output_size, return_indices)(input)
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def adaptive_avg_pool1d(input, output_size):
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r"""Applies a 1D adaptive average pooling over an input signal composed of
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several input planes.
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See :class:`~torch.nn.AdaptiveAvgPool1d` for details and output shape.
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Args:
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output_size: the target output size (single integer)
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"""
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return _functions.thnn.AdaptiveAvgPool1d(output_size)(input)
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def adaptive_avg_pool2d(input, output_size):
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r"""Applies a 2D adaptive average pooling over an input signal composed of
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several input planes.
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See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.
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Args:
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output_size: the target output size (single integer or double-integer tuple)
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"""
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return _functions.thnn.AdaptiveAvgPool2d(output_size)(input)
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# Activation functions
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def dropout(input, p=0.5, training=False, inplace=False):
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return _functions.dropout.Dropout(p, training, inplace)(input)
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def threshold(input, threshold, value, inplace=False):
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return _functions.thnn.auto.Threshold(threshold, value, inplace)(input)
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def relu(input, inplace=False):
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return _functions.thnn.auto.Threshold(0, 0, inplace)(input)
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def hardtanh(input, min_val=-1., max_val=1., inplace=False):
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return _functions.thnn.auto.Hardtanh(min_val, max_val, inplace)(input)
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def relu6(input, inplace=False):
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return _functions.thnn.auto.Hardtanh(0, 6, inplace)(input)
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def elu(input, alpha=1., inplace=False):
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return _functions.thnn.auto.ELU(alpha, inplace)(input)
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def leaky_relu(input, negative_slope=1e-2, inplace=False):
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return _functions.thnn.auto.LeakyReLU(negative_slope, inplace)(input)
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def prelu(input, weight):
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return _functions.thnn.PReLU()(input, weight)
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def rrelu(input, lower=1. / 8, upper=1. / 3, training=False, inplace=False):
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return _functions.thnn.RReLU(lower, upper, training, inplace)(input)
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def logsigmoid(input):
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return _functions.thnn.LogSigmoid()(input)
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def hardshrink(input, lambd=0.5):
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return _functions.thnn.auto.Hardshrink(lambd)(input)
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def tanhshrink(input):
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return input - _autograd_functions.Tanh()(input)
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def softsign(input):
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return _functions.activation.Softsign()(input)
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def softplus(input, beta=1, threshold=20):
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return _functions.thnn.auto.Softplus(beta, threshold)(input)
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def softmin(input):
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return _functions.thnn.Softmin()(input)
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def softmax(input):
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return _functions.thnn.auto.Softmax()(input)
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def softshrink(input, lambd=0.5):
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return _functions.thnn.auto.Softshrink(lambd)(input)
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def log_softmax(input):
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return _functions.thnn.LogSoftmax()(input)
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def tanh(input):
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return _autograd_functions.Tanh()(input)
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def sigmoid(input):
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return _autograd_functions.Sigmoid()(input)
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# etc.
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def linear(input, weight, bias=None):
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if bias is None:
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return _functions.linear.Linear.apply(input, weight)
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else:
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return _functions.linear.Linear.apply(input, weight, bias)
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def bilinear(input1, input2, weight, bias=None):
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state = _functions.linear.Bilinear()
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if bias is None:
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return state(input1, input2, weight)
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else:
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return state(input1, input2, weight, bias)
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def batch_norm(input, running_mean, running_var, weight=None, bias=None,
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training=False, momentum=0.1, eps=1e-5):
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f = torch._C._functions.BatchNorm(running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled)
|
|
return f(input, weight, bias)
|
|
|
|
|
|
# loss
|
|
|
|
def nll_loss(input, target, weight=None, size_average=True):
|
|
r"""The negative log likelihood loss.
|
|
|
|
See :class:`~torch.nn.NLLLoss` for details.
|
|
|
|
Args:
|
|
input: :math:`(N, C)` where `C = number of classes` or `(N, C, H, W)` in case of 2D - Loss
|
|
target: :math:`(N)` where each value is `0 <= targets[i] <= C-1`
|
|
weight (Variable, optional): a manual rescaling weight given to each
|
|
class. If given, has to be a Variable of size "nclasses"
|
|
size_average (bool, optional): By default, the losses are averaged
|
|
over observations for each minibatch. However, if the field
|
|
sizeAverage is set to False, the losses are instead summed
|
|
for each minibatch.
|
|
|
|
Attributes:
|
|
weight: the class-weights given as input to the constructor
|
|
|
|
Example:
|
|
>>> # input is of size nBatch x nClasses = 3 x 5
|
|
>>> input = autograd.Variable(torch.randn(3, 5))
|
|
>>> # each element in target has to have 0 <= value < nclasses
|
|
>>> target = autograd.Variable(torch.LongTensor([1, 0, 4]))
|
|
>>> output = F.nll_loss(F.log_softmax(input), target)
|
|
>>> output.backward()
|
|
"""
|
|
dim = input.dim()
|
|
if dim == 2:
|
|
f = _functions.thnn.NLLLoss(size_average, weight=weight)
|
|
elif dim == 4:
|
|
f = _functions.thnn.NLLLoss2d(size_average, weight=weight)
|
|
else:
|
|
raise ValueError('Expected 2 or 4 dimensions (got {})'.format(dim))
|
|
return f(input, target)
|
|
|
|
|
|
def kl_div(input, target, size_average=True):
|
|
r"""The `Kullback-Leibler divergence`_ Loss.
|
|
|
|
See :class:`~torch.nn.KLDivLoss` for details.
|
|
|
|
Args:
|
|
input: Variable of arbitrary shape
|
|
target: Variable of the same shape as input
|
|
size_average: if True the output is divided by the number of elements
|
|
in input tensor
|
|
"""
|
|
return _functions.thnn.KLDivLoss(size_average)(input, target)
|
|
|
|
|
|
def cross_entropy(input, target, weight=None, size_average=True):
|
|
r"""This criterion combines `log_softmax` and `nll_loss` in one single class.
|
|
|
|
See :class:`torch.nn.CrossEntropyLoss` for details.
|
|
|
|
Args:
|
|
input: Variable :math:`(N, C)` where `C = number of classes`
|
|
target: Variable :math:`(N)` where each value is `0 <= targets[i] <= C-1`
|
|
weight (Tensor, optional): a manual rescaling weight given to each
|
|
class. If given, has to be a Tensor of size "nclasses"
|
|
size_average (bool, optional): By default, the losses are averaged
|
|
over observations for each minibatch. However, if the field
|
|
sizeAverage is set to False, the losses are instead summed
|
|
for each minibatch.
|
|
"""
|
|
return nll_loss(log_softmax(input), target, weight, size_average)
|
|
|
|
|
|
def binary_cross_entropy(input, target, weight=None, size_average=True):
|
|
r"""Function that measures the Binary Cross Entropy
|
|
between the target and the output:
|
|
|
|
See :class:`~torch.nn.BCELoss` for details.
|
|
|
|
Args:
|
|
input: Variable of arbitrary shape
|
|
target: Variable of the same shape as input
|
|
weight (Variable, optional): a manual rescaling weight
|
|
if provided it's repeated to match input tensor shape
|
|
size_average (bool, optional): By default, the losses are averaged
|
|
over observations for each minibatch. However, if the field
|
|
sizeAverage is set to False, the losses are instead summed
|
|
for each minibatch.
|
|
"""
|
|
return _functions.thnn.BCELoss(size_average, weight=weight)(input, target)
|
|
|
|
|
|
def smooth_l1_loss(input, target, size_average=True):
|
|
return _functions.thnn.SmoothL1Loss(size_average)(input, target)
|
|
|
|
|
|
def pixel_shuffle(input, upscale_factor):
|
|
r"""Rearranges elements in a tensor of shape ``[*, C*r^2, H, W]`` to a
|
|
tensor of shape ``[C, H*r, W*r]``.
|
|
|
|
See :class:`~torch.nn.PixelShuffle` for details.
|
|
|
|
Args:
|
|
input (Variable): Input
|
|
upscale_factor (int): factor to increase spatial resolution by
|
|
|
|
Examples:
|
|
>>> ps = nn.PixelShuffle(3)
|
|
>>> input = autograd.Variable(torch.Tensor(1, 9, 4, 4))
|
|
>>> output = ps(input)
|
|
>>> print(output.size())
|
|
torch.Size([1, 1, 12, 12])
|
|
"""
|
|
batch_size, channels, in_height, in_width = input.size()
|
|
channels //= upscale_factor ** 2
|
|
|
|
out_height = in_height * upscale_factor
|
|
out_width = in_width * upscale_factor
|
|
|
|
input_view = input.contiguous().view(
|
|
batch_size, channels, upscale_factor, upscale_factor,
|
|
in_height, in_width)
|
|
|
|
shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous()
|
|
return shuffle_out.view(batch_size, channels, out_height, out_width)
|
|
|
|
|
|
def upsample_nearest(input, size=None, scale_factor=None):
|
|
"""Upsamples the input, using nearest neighbours' pixel values.
|
|
|
|
Currently only spatial upsampling is supported (i.e. expected inputs
|
|
are 4 dimensional).
|
|
|
|
Args:
|
|
input (Variable): input
|
|
size (int or Tuple[int, int]): output spatial size.
|
|
scale_factor (int): multiplier for spatial size. Has to be an integer.
|
|
"""
|
|
return _functions.thnn.UpsamplingNearest2d(size, scale_factor)(input)
|
|
|
|
|
|
def upsample_bilinear(input, size=None, scale_factor=None):
|
|
"""Upscales the input, using the bilinear upsampling.
|
|
|
|
Currently only spatial upsampling is supported (i.e. expected inputs
|
|
are 4 dimensional).
|
|
|
|
Args:
|
|
input (Variable): input
|
|
size (int or Tuple[int, int]): output spatial size.
|
|
scale_factor (int or Tuple[int, int]): multiplier for spatial size
|
|
"""
|
|
return _functions.thnn.UpsamplingBilinear2d(size, scale_factor)(input)
|
|
|
|
|
|
def _check_bilinear_2d_scale_factor(scale_factor):
|
|
scale_factor = _pair(scale_factor)
|
|
try:
|
|
assert len(scale_factor) == 2
|
|
assert all(isinstance(s, Integral) and s >= 1 for s in scale_factor)
|
|
except AssertionError as e:
|
|
raise ValueError('scale_factor must be a non-negative integer, '
|
|
'or a tuple of non-negative integers for bilinear upsamplings, but got: '
|
|
'{}'.format(scale_factor))
|
|
return scale_factor
|
|
|
|
|
|
def pad(input, pad, mode='constant', value=0):
|
|
"""Pads tensor.
|
|
|
|
Currently only 2D and 3D padding supported.
|
|
In case of 4D input tensor pad should be in form (pad_l, pad_r, pad_t, pad_b )
|
|
In case of 5D pad should be (pleft, pright, ptop, pbottom, pfront, pback)
|
|
|
|
Args:
|
|
input (Variable): 4D or 5D tensor
|
|
pad (tuple): 4-elem or 6-elem tuple
|
|
mode: 'constant', 'reflect' or 'replicate'
|
|
value: fill value for 'constant' padding
|
|
"""
|
|
if input.dim() == 4:
|
|
assert len(pad) == 4, '4D tensors expect 4 values for padding'
|
|
if mode == 'constant':
|
|
return ConstantPad2d(pad, value)(input)
|
|
elif mode == 'reflect':
|
|
return _functions.thnn.ReflectionPad2d(*pad)(input)
|
|
elif mode == 'replicate':
|
|
return _functions.thnn.ReplicationPad2d(*pad)(input)
|
|
elif input.dim() == 5:
|
|
assert len(pad) == 6, '5D tensors expect 6 values for padding'
|
|
if mode == 'constant':
|
|
raise NotImplementedError
|
|
elif mode == 'reflect':
|
|
raise NotImplementedError
|
|
elif mode == 'replicate':
|
|
return _functions.thnn.ReplicationPad3d(*pad)(input)
|
|
else:
|
|
raise NotImplementedError("Only 4D and 5D padding is supported for now")
|
|
|
|
|
|
# distance
|
|
|
|
def pairwise_distance(x1, x2, p=2, eps=1e-6):
|
|
r"""
|
|
Computes the batchwise pairwise distance between vectors v1,v2:
|
|
|
|
.. math ::
|
|
\Vert x \Vert _p := \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}
|
|
|
|
Args:
|
|
x1: first input tensor
|
|
x2: second input tensor
|
|
p: the norm degree. Default: 2
|
|
|
|
Shape:
|
|
- Input: :math:`(N, D)` where `D = vector dimension`
|
|
- Output: :math:`(N, 1)`
|
|
|
|
>>> input1 = autograd.Variable(torch.randn(100, 128))
|
|
>>> input2 = autograd.Variable(torch.randn(100, 128))
|
|
>>> output = F.pairwise_distance(input1, input2, p=2)
|
|
>>> output.backward()
|
|
"""
|
|
assert x1.size() == x2.size(), "Input sizes must be equal."
|
|
assert x1.dim() == 2, "Input must be a 2D matrix."
|
|
diff = torch.abs(x1 - x2)
|
|
out = torch.pow(diff + eps, p).sum(dim=1)
|
|
return torch.pow(out, 1. / p)
|
|
|
|
|
|
def triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, swap=False):
|
|
r"""Creates a criterion that measures the triplet loss given an input tensors x1, x2, x3
|
|
and a margin with a value greater than 0.
|
|
This is used for measuring a relative similarity between samples. A triplet is composed by
|
|
`a`, `p` and `n`: anchor, positive examples and negative example respectively.
|
|
The shape of all input variables should be :math:`(N, D)`.
|
|
|
|
The distance swap is described in detail in the paper `Learning shallow convolutional feature descriptors with
|
|
triplet losses`_ by V. Balntas, E. Riba et al.
|
|
|
|
.. math::
|
|
L(a, p, n) = \frac{1}{N} \left( \sum_{i=1}^N \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} \right)
|
|
|
|
where :math:`d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`.
|
|
|
|
Args:
|
|
anchor: anchor input tensor
|
|
positive: positive input tensor
|
|
negative: negative input tensor
|
|
p: the norm degree. Default: 2
|
|
eps: small epsilon value to avoid numerical issues
|
|
swap: compute distance swap
|
|
|
|
Shape:
|
|
- Input: :math:`(N, D)` where `D = vector dimension`
|
|
- Output: :math:`(N, 1)`
|
|
|
|
>>> input1 = autograd.Variable(torch.randn(100, 128))
|
|
>>> input2 = autograd.Variable(torch.randn(100, 128))
|
|
>>> input3 = autograd.Variable(torch.randn(100, 128))
|
|
>>> output = F.triplet_margin_loss(input1, input2, input3, p=2)
|
|
>>> output.backward()
|
|
|
|
.. _Learning shallow convolutional feature descriptors with triplet losses:
|
|
http://www.iis.ee.ic.ac.uk/%7Evbalnt/shallow_descr/TFeat_paper.pdf
|
|
"""
|
|
assert anchor.size() == positive.size(), "Input sizes between positive and negative must be equal."
|
|
assert anchor.size() == negative.size(), "Input sizes between anchor and negative must be equal."
|
|
assert positive.size() == negative.size(), "Input sizes between positive and negative must be equal."
|
|
assert anchor.dim() == 2, "Inputd must be a 2D matrix."
|
|
assert margin > 0.0, 'Margin should be positive value.'
|
|
d_p = pairwise_distance(anchor, positive, p, eps)
|
|
d_n = pairwise_distance(anchor, negative, p, eps)
|
|
if swap:
|
|
d_s = pairwise_distance(positive, negative, p, eps)
|
|
d_n = torch.min(d_n, d_s)
|
|
|
|
dist_hinge = torch.clamp(margin + d_p - d_n, min=0.0)
|
|
loss = torch.mean(dist_hinge)
|
|
return loss
|