【神经网络常用损失函数】

神经网络常用损失函数

FocalLoss

class FocalLoss(nn.Module):
    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        super().__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = 'none'  # required to apply FL to each element

    def forward(self, pred, true):
        loss = self.loss_fcn(pred, true)
        # p_t = torch.exp(-loss)
        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability

        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
        pred_prob = torch.sigmoid(pred)  # prob from logits
        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
        modulating_factor = (1.0 - p_t) ** self.gamma
        loss *= alpha_factor * modulating_factor

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        else:  # 'none'
            return loss

DiceLoss

class DiceLoss(nn.Module):
    def __init__(self, n_classes):
        super(DiceLoss, self).__init__()
        self.n_classes = n_classes

    def _one_hot_encoder(self, input_tensor):
        tensor_list = []
        for i in range(self.n_classes):
            temp_prob = input_tensor == i  # * torch.ones_like(input_tensor)
            tensor_list.append(temp_prob.unsqueeze(1))
        output_tensor = torch.cat(tensor_list, dim=1)
        return output_tensor.float()

    def _dice_loss(self, score, target):
        target = target.float()
        smooth = 1e-5
        intersect = torch.sum(score * target)
        y_sum = torch.sum(target * target)
        z_sum = torch.sum(score * score)
        loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth)
        loss = 1 - loss
        return loss

    def forward(self, inputs, target, weight=None, softmax=False):
        if softmax:
            inputs = torch.softmax(inputs, dim=1)
        target = self._one_hot_encoder(target)
        if weight is None:
            weight = [1] * self.n_classes
        assert inputs.size() == target.size(), 'predict {} & target {} shape do not match'.format(inputs.size(), target.size())
        class_wise_dice = []
        loss = 0.0
        for i in range(0, self.n_classes):
            dice = self._dice_loss(inputs[:, i], target[:, i])
            class_wise_dice.append(1.0 - dice.item())
            loss += dice * weight[i]
        return loss / self.n_classes

Lovasz_softmax损失

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
from itertools import  filterfalse as ifilterfalse


# https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytorch/lovasz_losses.py
def lovasz_grad(gt_sorted):
    """
    Computes gradient of the Lovasz extension w.r.t sorted errors
    See Alg. 1 in paper
    """
    p = len(gt_sorted)
    gts = gt_sorted.sum()
    intersection = gts - gt_sorted.float().cumsum(0)
    union = gts + (1 - gt_sorted).float().cumsum(0)
    jaccard = 1. - intersection / union
    if p > 1: # cover 1-pixel case
        jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
    return jaccard


def iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):
    """
    IoU for foreground class
    binary: 1 foreground, 0 background
    """
    if not per_image:
        preds, labels = (preds,), (labels,)
    ious = []
    for pred, label in zip(preds, labels):
        intersection = ((label == 1) & (pred == 1)).sum()
        union = ((label == 1) | ((pred == 1) & (label != ignore))).sum()
        if not union:
            iou = EMPTY
        else:
            iou = float(intersection) / float(union)
        ious.append(iou)
    iou = mean(ious)    # mean accross images if per_image
    return 100 * iou


def iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False):
    """
    Array of IoU for each (non ignored) class
    """
    if not per_image:
        preds, labels = (preds,), (labels,)
    ious = []
    for pred, label in zip(preds, labels):
        iou = []    
        for i in range(C):
            if i != ignore: # The ignored label is sometimes among predicted classes (ENet - CityScapes)
                intersection = ((label == i) & (pred == i)).sum()
                union = ((label == i) | ((pred == i) & (label != ignore))).sum()
                if not union:
                    iou.append(EMPTY)
                else:
                    iou.append(float(intersection) / float(union))
        ious.append(iou)
    ious = [mean(iou) for iou in zip(*ious)] # mean accross images if per_image
    return 100 * np.array(ious)


# --------------------------- BINARY LOSSES ---------------------------


def lovasz_hinge(logits, labels, per_image=True, ignore=None):
    """
    Binary Lovasz hinge loss
      logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
      labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
      per_image: compute the loss per image instead of per batch
      ignore: void class id
    """
    if per_image:
        loss = mean(lovasz_hinge_flat(*flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore))
                          for log, lab in zip(logits, labels))
    else:
        loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))
    return loss


def lovasz_hinge_flat(logits, labels):
    """
    Binary Lovasz hinge loss
      logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
      labels: [P] Tensor, binary ground truth labels (0 or 1)
      ignore: label to ignore
    """
    if len(labels) == 0:
        # only void pixels, the gradients should be 0
        return logits.sum() * 0.
    signs = 2. * labels.float() - 1.
    errors = (1. - logits * Variable(signs))
    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
    perm = perm.data
    gt_sorted = labels[perm]
    grad = lovasz_grad(gt_sorted)
    loss = torch.dot(F.relu(errors_sorted), Variable(grad))
    return loss


def flatten_binary_scores(scores, labels, ignore=None):
    """
    Flattens predictions in the batch (binary case)
    Remove labels equal to 'ignore'
    """
    scores = scores.view(-1)
    labels = labels.view(-1)
    if ignore is None:
        return scores, labels
    valid = (labels != ignore)
    vscores = scores[valid]
    vlabels = labels[valid]
    return vscores, vlabels


class StableBCELoss(torch.nn.modules.Module):
    def __init__(self):
         super(StableBCELoss, self).__init__()
    def forward(self, input, target):
         neg_abs = - input.abs()
         loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
         return loss.mean()


def binary_xloss(logits, labels, ignore=None):
    """
    Binary Cross entropy loss
      logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
      labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
      ignore: void class id
    """
    logits, labels = flatten_binary_scores(logits, labels, ignore)
    loss = StableBCELoss()(logits, Variable(labels.float()))
    return loss


# --------------------------- MULTICLASS LOSSES ---------------------------


def lovasz_softmax(probas, labels, classes='present', per_image=False, ignore=None):
    """
    Multi-class Lovasz-Softmax loss
      probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1).
              Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
      labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
      per_image: compute the loss per image instead of per batch
      ignore: void class labels
    """
    if per_image:
        loss = mean(lovasz_softmax_flat(*flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes)
                          for prob, lab in zip(probas, labels))
    else:
        loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore), classes=classes)
    return loss


def lovasz_softmax_flat(probas, labels, classes='present'):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
    """
    if probas.numel() == 0:
        # only void pixels, the gradients should be 0
        return probas * 0.
    #获取类别数
    C = probas.size(1)
    losses = []
    class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
    for c in class_to_sum:
        fg = (labels == c).float() # foreground for class c
        if (classes is 'present' and fg.sum() == 0):
            continue
        if C == 1:
            if len(classes) > 1:
                raise ValueError('Sigmoid output possible only with 1 class')
            class_pred = probas[:, 0]
        else:
            class_pred = probas[:, c]
        errors = (Variable(fg) - class_pred).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
    return mean(losses)


def flatten_probas(probas, labels, ignore=None):
    """
    Flattens predictions in the batch
    """
    if probas.dim() == 3:
        # assumes output of a sigmoid layer
        B, H, W = probas.size()
        probas = probas.view(B, 1, H, W)
    B, C, H, W = probas.size()
    probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C)  # B * H * W, C = P, C
    labels = labels.view(-1)
    if ignore is None:
        return probas, labels
    valid = (labels != ignore)
    vprobas = probas[valid.nonzero().squeeze()]
    vlabels = labels[valid]
    return vprobas, vlabels

def xloss(logits, labels, ignore=None):
    """
    Cross entropy loss
    """
    return F.cross_entropy(logits, Variable(labels), ignore_index=255)


# --------------------------- HELPER FUNCTIONS ---------------------------
def isnan(x):
    return x != x
    
    
def mean(l, ignore_nan=False, empty=0):
    """
    nanmean compatible with generators.
    """
    l = iter(l)
    if ignore_nan:
        l = ifilterfalse(isnan, l)
    try:
        n = 1
        acc = next(l)
    except StopIteration:
        if empty == 'raise':
            raise ValueError('Empty mean')
        return empty
    for n, v in enumerate(l, 2):
        acc += v
    if n == 1:
        return acc
    return acc / n