Source code for torch.nn.modules.loss

from torch.autograd import Variable
import torch
from .module import Module
from .container import Sequential
from .activation import LogSoftmax
from .. import functional as F


def _assert_no_grad(variable):
    assert not variable.requires_grad, \
        "nn criterions don't compute the gradient w.r.t. targets - please " \
        "mark these variables as volatile or not requiring gradients"


class _Loss(Module):
    def __init__(self, size_average=True):
        super(_Loss, self).__init__()
        self.size_average = size_average

    def forward(self, input, target):
        _assert_no_grad(target)
        backend_fn = getattr(self._backend, type(self).__name__)
        return backend_fn(self.size_average)(input, target)


class _WeightedLoss(_Loss):
    def __init__(self, weight=None, size_average=True):
        super(_WeightedLoss, self).__init__(size_average)
        self.register_buffer('weight', weight)

    def forward(self, input, target):
        _assert_no_grad(target)
        backend_fn = getattr(self._backend, type(self).__name__)
        return backend_fn(self.size_average, weight=self.weight)(input, target)


[docs]class L1Loss(_Loss): r"""Creates a criterion that measures the mean absolute value of the element-wise difference between input `x` and target `y`: :math:`{loss}(x, y) = 1/n \sum |x_i - y_i|` `x` and `y` arbitrary shapes with a total of `n` elements each. The sum operation still operates over all the elements, and divides by `n`. The division by `n` can be avoided if one sets the constructor argument `size_average=False` """ pass
[docs]class NLLLoss(_WeightedLoss): r"""The negative log likelihood loss. It is useful to train a classification problem with n classes If provided, the optional argument `weights` should be a 1D Tensor assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set. The input given through a forward call is expected to contain log-probabilities of each class: input has to be a 2D Tensor of size `(minibatch, n)` Obtaining log-probabilities in a neural network is easily achieved by adding a `LogSoftmax` layer in the last layer of your network. You may use `CrossEntropyLoss` instead, if you prefer not to add an extra layer. The target that this loss expects is a class index `(0 to N-1, where N = number of classes)` The loss can be described as:: loss(x, class) = -x[class] or in the case of the weights argument it is specified as follows:: loss(x, class) = -weights[class] * x[class] Args: 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 size_average is set to False, the losses are instead summed for each minibatch. Shape: - Input: :math:`(N, C)` where `C = number of classes` - Target: :math:`(N)` where each value is `0 <= targets[i] <= C-1` Attributes: weight: the class-weights given as input to the constructor Examples:: >>> m = nn.LogSoftmax() >>> loss = nn.NLLLoss() >>> # input is of size nBatch x nClasses = 3 x 5 >>> input = autograd.Variable(torch.randn(3, 5), requires_grad=True) >>> # each element in target has to have 0 <= value < nclasses >>> target = autograd.Variable(torch.LongTensor([1, 0, 4])) >>> output = loss(m(input), target) >>> output.backward() """ def forward(self, input, target): _assert_no_grad(target) return F.nll_loss(input, target, self.weight, self.size_average)
[docs]class NLLLoss2d(_WeightedLoss): r"""This is negative log likehood loss, but for image inputs. It computes NLL loss per-pixel. Args: weight (Tensor, optional): a manual rescaling weight given to each class. If given, has to be a 1D Tensor having as many elements, as there are classes. size_average: By default, the losses are averaged over observations for each minibatch. However, if the field size_average is set to False, the losses are instead summed for each minibatch. Default: True Shape: - Input: :math:`(N, C, H, W)` where `C = number of classes` - Target: :math:`(N, H, W)` where each value is `0 <= targets[i] <= C-1` Examples: >>> m = nn.Conv2d(16, 32, (3, 3)).float() >>> loss = nn.NLLLoss2d() >>> # input is of size nBatch x nClasses x height x width >>> input = autograd.Variable(torch.randn(3, 16, 10, 10)) >>> # each element in target has to have 0 <= value < nclasses >>> target = autograd.Variable(torch.LongTensor(3, 8, 8).random_(0, 4)) >>> output = loss(m(input), target) >>> output.backward() """ pass
[docs]class KLDivLoss(_WeightedLoss): r"""The `Kullback-Leibler divergence`_ Loss KL divergence is a useful distance measure for continuous distributions and is often useful when performing direct regression over the space of (discretely sampled) continuous output distributions. As with `NLLLoss`, the `input` given is expected to contain *log-probabilities*, however unlike `ClassNLLLoss`, `input` is not restricted to a 2D Tensor, because the criterion is applied element-wise. This criterion expects a `target` `Tensor` of the same size as the `input` `Tensor`. The loss can be described as: .. math:: loss(x, target) = 1/n \sum(target_i * (log(target_i) - x_i)) By default, the losses are averaged for each minibatch over observations **as well as** over dimensions. However, if the field `size_average` is set to `False`, the losses are instead summed. .. _Kullback-Leibler divergence: https://en.wikipedia.org/wiki/Kullback-Leibler_divergence """ pass
[docs]class MSELoss(_Loss): r"""Creates a criterion that measures the mean squared error between `n` elements in the input `x` and target `y`: :math:`{loss}(x, y) = 1/n \sum |x_i - y_i|^2` `x` and `y` arbitrary shapes with a total of `n` elements each. The sum operation still operates over all the elements, and divides by `n`. The division by `n` can be avoided if one sets the internal variable `size_average` to `False`. """ pass
[docs]class BCELoss(_WeightedLoss): r"""Creates a criterion that measures the Binary Cross Entropy between the target and the output: .. math:: loss(o, t) = - 1/n \sum_i (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i])) or in the case of the weights argument being specified: .. math:: loss(o, t) = - 1/n \sum_i weights[i] * (t[i] * log(o[i]) + (1 - t[i]) * log(1 - o[i])) This is used for measuring the error of a reconstruction in for example an auto-encoder. Note that the targets `t[i]` should be numbers between 0 and 1. By default, the losses are averaged for each minibatch over observations *as well as* over dimensions. However, if the field `size_average` is set to `False`, the losses are instead summed. """ pass
[docs]class BCEWithLogitsLoss(Module): r"""This loss combines a `Sigmoid` layer and the `BCELoss` in one single class. This version is more numerically stable than using a plain `Sigmoid` followed by a `BCELoss` as, by combining the operations into one layer, we take advantage of the log-sum-exp trick for numerical stability. This Binary Cross Entropy between the target and the output logits (no sigmoid applied) is: .. math:: loss(o, t) = - 1/n \sum_i (t[i] * log(sigmoid(o[i])) + (1 - t[i]) * log(1 - sigmoid(o[i]))) or in the case of the weights argument being specified: .. math:: loss(o, t) = - 1/n \sum_i weights[i] * (t[i] * log(sigmoid(o[i])) + (1 - t[i]) * log(1 - sigmoid(o[i]))) This is used for measuring the error of a reconstruction in for example an auto-encoder. Note that the targets `t[i]` should be numbers between 0 and 1. By default, the losses are averaged for each minibatch over observations *as well as* over dimensions. However, if the field `size_average` is set to `False`, the losses are instead summed. """ def __init__(self, weight=None, size_average=True): super(BCEWithLogitsLoss, self).__init__() self.size_average = size_average self.register_buffer('weight', weight) def forward(self, input, target): if self.weight is not None: return F.binary_cross_entropy_with_logits(input, target, Variable(self.weight), self.size_average) else: return F.binary_cross_entropy_with_logits(input, target, size_average=self.size_average)
[docs]class HingeEmbeddingLoss(_Loss): r"""Measures the loss given an input `x` which is a 2D mini-batch tensor and a labels `y`, a 1D tensor containg values (`1` or `-1`). This is usually used for measuring whether two inputs are similar or dissimilar, e.g. using the L1 pairwise distance, and is typically used for learning nonlinear embeddings or semi-supervised learning:: { x_i, if y_i == 1 loss(x, y) = 1/n { { max(0, margin - x_i), if y_i == -1 `x` and `y` arbitrary shapes with a total of `n` elements each the sum operation still operates over all the elements, and divides by `n`. The division by `n` can be avoided if one sets the internal variable `size_average=False`. The `margin` has a default value of `1`, or can be set in the constructor. """ def __init__(self, margin=1.0, size_average=True): super(HingeEmbeddingLoss, self).__init__() self.margin = margin self.size_average = size_average def forward(self, input, target): return self._backend.HingeEmbeddingLoss(self.margin, self.size_average)(input, target)
[docs]class MultiLabelMarginLoss(_Loss): r"""Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input `x` (a 2D mini-batch `Tensor`) and output `y` (which is a 2D `Tensor` of target class indices). For each sample in the mini-batch:: loss(x, y) = sum_ij(max(0, 1 - (x[y[j]] - x[i]))) / x.size(0) where `i == 0` to `x.size(0)`, `j == 0` to `y.size(0)`, `y[j] != 0`, and `i != y[j]` for all `i` and `j`. `y` and `x` must have the same size. The criterion only considers the first non zero `y[j]` targets. This allows for different samples to have variable amounts of target classes """ pass
[docs]class SmoothL1Loss(_Loss): r"""Creates a criterion that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise. It is less sensitive to outliers than the `MSELoss` and in some cases prevents exploding gradients (e.g. see "Fast R-CNN" paper by Ross Girshick). Also known as the Huber loss:: { 0.5 * (x_i - y_i)^2, if |x_i - y_i| < 1 loss(x, y) = 1/n \sum { { |x_i - y_i| - 0.5, otherwise `x` and `y` arbitrary shapes with a total of `n` elements each the sum operation still operates over all the elements, and divides by `n`. The division by `n` can be avoided if one sets the internal variable `size_average` to `False` """ pass
[docs]class SoftMarginLoss(_Loss): r"""Creates a criterion that optimizes a two-class classification logistic loss between input `x` (a 2D mini-batch Tensor) and target `y` (which is a tensor containing either `1` or `-1`). :: loss(x, y) = sum_i (log(1 + exp(-y[i]*x[i]))) / x.nelement() The normalization by the number of elements in the input can be disabled by setting `self.size_average` to `False`. """ pass
[docs]class CrossEntropyLoss(_WeightedLoss): r"""This criterion combines `LogSoftMax` and `NLLLoss` in one single class. It is useful when training a classification problem with `n` classes. If provided, the optional argument `weights` should be a 1D `Tensor` assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set. The `input` is expected to contain scores for each class. `input` has to be a 2D `Tensor` of size `batch x n`. This criterion expects a class index (0 to nClasses-1) as the `target` for each value of a 1D tensor of size `n` The loss can be described as:: loss(x, class) = -log(exp(x[class]) / (\sum_j exp(x[j]))) = -x[class] + log(\sum_j exp(x[j])) or in the case of the `weights` argument being specified:: loss(x, class) = weights[class] * (-x[class] + log(\sum_j exp(x[j]))) The losses are averaged across observations for each minibatch. Shape: - Input: :math:`(N, C)` where `C = number of classes` - Target: :math:`(N)` where each value is `0 <= targets[i] <= C-1` """ def forward(self, input, target): _assert_no_grad(target) return F.cross_entropy(input, target, self.weight, self.size_average)
[docs]class MultiLabelSoftMarginLoss(_WeightedLoss): r"""Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input `x` (a 2D mini-batch `Tensor`) and target `y` (a binary 2D `Tensor`). For each sample in the minibatch:: loss(x, y) = - sum_i (y[i] * log( 1 / (1 + exp(-x[i])) ) + ( (1-y[i]) * log(exp(-x[i]) / (1 + exp(-x[i])) ) ) where `i == 0` to `x.nElement()-1`, `y[i] in {0,1}`. `y` and `x` must have the same size. """ def forward(self, input, target): return F.binary_cross_entropy(torch.sigmoid(input), target, self.weight, self.size_average)
[docs]class CosineEmbeddingLoss(Module): r"""Creates a criterion that measures the loss given an input tensors x1, x2 and a `Tensor` label `y` with values 1 or -1. This is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning. `margin` should be a number from `-1` to `1`, `0` to `0.5` is suggested. If `margin` is missing, the default value is `0`. The loss function for each sample is:: { 1 - cos(x1, x2), if y == 1 loss(x, y) = { { max(0, cos(x1, x2) - margin), if y == -1 If the internal variable `size_average` is equal to `True`, the loss function averages the loss over the batch samples; if `size_average` is `False`, then the loss function sums over the batch samples. By default, `size_average = True`. """ def __init__(self, margin=0, size_average=True): super(CosineEmbeddingLoss, self).__init__() self.margin = margin self.size_average = size_average def forward(self, input1, input2, target): return self._backend.CosineEmbeddingLoss(self.margin, self.size_average)(input1, input2, target)
[docs]class MarginRankingLoss(Module): r"""Creates a criterion that measures the loss given inputs `x1`, `x2`, two 1D mini-batch `Tensor`s, and a label 1D mini-batch tensor `y` with values (`1` or `-1`). If `y == 1` then it assumed the first input should be ranked higher (have a larger value) than the second input, and vice-versa for `y == -1`. The loss function for each sample in the mini-batch is:: loss(x, y) = max(0, -y * (x1 - x2) + margin) if the internal variable `size_average = True`, the loss function averages the loss over the batch samples; if `size_average = False`, then the loss function sums over the batch samples. By default, `size_average` equals to `True`. """ def __init__(self, margin=0, size_average=True): super(MarginRankingLoss, self).__init__() self.margin = margin self.size_average = size_average def forward(self, input1, input2, target): return self._backend.MarginRankingLoss(self.margin, self.size_average)(input1, input2, target)
[docs]class MultiMarginLoss(Module): r"""Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input `x` (a 2D mini-batch `Tensor`) and output `y` (which is a 1D tensor of target class indices, `0` <= `y` <= `x.size(1)`): For each mini-batch sample:: loss(x, y) = sum_i(max(0, (margin - x[y] + x[i]))^p) / x.size(0) where `i == 0` to `x.size(0)` and `i != y`. Optionally, you can give non-equal weighting on the classes by passing a 1D `weights` tensor into the constructor. The loss function then becomes: loss(x, y) = sum_i(max(0, w[y] * (margin - x[y] - x[i]))^p) / x.size(0) By default, the losses are averaged over observations for each minibatch. However, if the field `size_average` is set to `False`, the losses are instead summed. """ def __init__(self, p=1, margin=1, weight=None, size_average=True): super(MultiMarginLoss, self).__init__() if p != 1 and p != 2: raise ValueError("only p == 1 and p == 2 supported") assert weight is None or weight.dim() == 1 self.p = p self.margin = margin self.size_average = size_average self.weight = weight def forward(self, input, target): return self._backend.MultiMarginLoss(self.size_average, self.p, self.margin, weight=self.weight)(input, target)
[docs]class TripletMarginLoss(Module): 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 Shape: - Input: :math:`(N, D)` where `D = vector dimension` - Output: :math:`(N, 1)` >>> triplet_loss = nn.TripletMarginLoss(margin=1.0, p=2) >>> input1 = autograd.Variable(torch.randn(100, 128)) >>> input2 = autograd.Variable(torch.randn(100, 128)) >>> input3 = autograd.Variable(torch.randn(100, 128)) >>> output = triplet_loss(input1, input2, input3) >>> output.backward() .. _Learning shallow convolutional feature descriptors with triplet losses: http://www.iis.ee.ic.ac.uk/%7Evbalnt/shallow_descr/TFeat_paper.pdf """ def __init__(self, margin=1.0, p=2, eps=1e-6, swap=False): super(TripletMarginLoss, self).__init__() self.margin = margin self.p = p self.eps = eps self.swap = swap def forward(self, anchor, positive, negative): return F.triplet_margin_loss(anchor, positive, negative, self.margin, self.p, self.eps, self.swap)
# TODO: L1HingeEmbeddingCriterion # TODO: MSECriterion weight # TODO: ClassSimplexCriterion