loss_function.py

from torch import nn
import torch
import torch.nn.functional as F
from torch.autograd import Variable

class BCEFocalLoss(torch.nn.Module):

    def __init__(self, gamma=2, alpha=0.8, reduction='elementwise_mean'):
        super().__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = reduction

    def forward(self, _input, target, epsilon = 1e-6,):
        pt = torch.sigmoid(_input)
        loss = - self.alpha * (1 - pt) ** self.gamma * target * torch.log(pt + epsilon) - \
            pt ** self.gamma * (1 - target) * torch.log(1 - pt +epsilon) * (1 - self.alpha)
        #if self.alpha: 
         #   loss = loss * self.alpha
        if self.reduction == 'elementwise_mean':
            loss = torch.mean(loss)
        elif self.reduction == 'sum':
            loss = torch.sum(loss)
        return loss

class weightbceloss(torch.nn.Module):

    def __init__(self, gamma=2, alpha=1, reduction='elementwise_mean'):
        super().__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = reduction

    def forward(self, _input, target, epsilon = 1e-6,):
        pt = torch.sigmoid(_input)
        loss = - self.alpha * target * torch.log(pt + epsilon) - \
            (1 - target) * torch.log(1 - pt +epsilon) * (1 - self.alpha)
        #if self.alpha: 
         #   loss = loss * self.alpha
        if self.reduction == 'elementwise_mean':
            loss = torch.mean(loss)
        elif self.reduction == 'sum':
            loss = torch.sum(loss)
        return loss 

class weightbceloss1(torch.nn.Module):

    def __init__(self, gamma=2, alpha=1, reduction='elementwise_mean'):
        super().__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = reduction

    def forward(self, outputs_unlabel_unaug, outputs_unlabel, epsilon = 1e-6,):
        y1 = torch.sigmoid(outputs_unlabel_unaug)
        y2 = torch.sigmoid(outputs_unlabel)
        # loss = - self.alpha * (pt - target) * torch.log(1 - target + epsilon)
        loss = - (1 - (y1 - y2)) * torch.log(1- (y1 - y2)+epsilon)
        #if self.alpha: 
         #   loss = loss * self.alpha
        if self.reduction == 'elementwise_mean':
            loss = torch.mean(loss)
        elif self.reduction == 'sum':
            loss = torch.sum(loss)
        return loss 

class MSE_Loss(nn.Module):
    def __init__(self):
        super(MSE_Loss, self).__init__()
        self.criterion = nn.MSELoss()


    def forward(self, model_output, targets):
        #targets[targets == 0] = -1

        # torch.empty(3, dtype=torch.long)
        # model_output = model_output.long()
        # targets = targets.long()
        # print(model_output)
        # print(F.sigmoid(model_output))
        # print(targets)
        # print('kkk')
        # model_output =torch.LongTensor(model_output.cpu())
        # targets =torch.LongTensor(targets.cpu())
        # model_output = model_output.type(torch.LongTensor)
        # targets = targets.type(torch.LongTensor)
        loss = self.criterion(model_output, targets)

       
        return loss


def cross_entropy_3D(input, target, weight=None, size_average=True):
    n, c, h, w, s = input.size()
    log_p = F.log_softmax(input, dim=1)
    log_p = log_p.transpose(1, 2).transpose(2, 3).transpose(3, 4).contiguous().view(-1, c)
    target = target.view(target.numel())
    loss = F.nll_loss(log_p, target, weight=weight, size_average=False)
    if size_average:
        loss /= float(target.numel())
    return loss


class Binary_Loss(nn.Module):
    def __init__(self):
        super(Binary_Loss, self).__init__()
        self.criterion = nn.BCEWithLogitsLoss()


    def forward(self, model_output, targets):
        #targets[targets == 0] = -1

        # torch.empty(3, dtype=torch.long)
        # model_output = model_output.long()
        # targets = targets.long()
        # print(model_output)
        # print(F.sigmoid(model_output))
        # print(targets)
        # print('kkk')
        # model_output =torch.LongTensor(model_output.cpu())
        # targets =torch.LongTensor(targets.cpu())
        # model_output = model_output.type(torch.LongTensor)
        # targets = targets.type(torch.LongTensor)
        loss = self.criterion(model_output, targets)

       
        return loss

def make_one_hot(input, num_classes):
    """Convert class index tensor to one hot encoding tensor.
    Args:
         input: A tensor of shape [N, 1, *]
         num_classes: An int of number of class
    Returns:
        A tensor of shape [N, num_classes, *]
    """
    shape = np.array(input.shape)
    shape[1] = num_classes
    shape = tuple(shape)
    result = torch.zeros(shape)
    result = result.scatter_(1, input.cpu(), 1)

    return result

class BinaryDiceLoss(nn.Module):
    """Dice loss of binary class
    Args:
        smooth: A float number to smooth loss, and avoid NaN error, default: 1
        p: Denominator value: \sum{x^p} + \sum{y^p}, default: 2
        predict: A tensor of shape [N, *]
        target: A tensor of shape same with predict
        reduction: Reduction method to apply, return mean over batch if 'mean',
            return sum if 'sum', return a tensor of shape [N,] if 'none'
    Returns:
        Loss tensor according to arg reduction
    Raise:
        Exception if unexpected reduction
    """
    def __init__(self, smooth=1, p=2, reduction='mean'):
        super(BinaryDiceLoss, self).__init__()
        self.smooth = smooth
        self.p = p
        self.reduction = reduction

    def forward(self, predict, target):
        assert predict.shape[0] == target.shape[0], "predict & target batch size don't match"
        predict = predict.contiguous().view(predict.shape[0], -1)
        target = target.contiguous().view(target.shape[0], -1)

        num = torch.sum(torch.mul(predict, target), dim=1) + self.smooth
        den = torch.sum(predict.pow(self.p) + target.pow(self.p), dim=1) + self.smooth

        loss = 1 - num / den

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        elif self.reduction == 'none':
            return loss
        else:
            raise Exception('Unexpected reduction {}'.format(self.reduction))
        
class DiceLoss(nn.Module):
    """Dice loss, need one hot encode input
    Args:
        weight: An array of shape [num_classes,]
        ignore_index: class index to ignore
        predict: A tensor of shape [N, C, *]
        target: A tensor of same shape with predict
        other args pass to BinaryDiceLoss
    Return:
        same as BinaryDiceLoss
    """
    def __init__(self, weight=None, ignore_index=None, **kwargs):
        super(DiceLoss, self).__init__()
        self.kwargs = kwargs
        self.weight = weight
        self.ignore_index = ignore_index

    def forward(self, predict, target):
        assert predict.shape == target.shape, 'predict & target shape do not match'
        dice = BinaryDiceLoss(**self.kwargs)
        total_loss = 0
        predict = F.softmax(predict, dim=1)

        for i in range(target.shape[1]):
            if i != self.ignore_index:
                dice_loss = dice(predict[:, i], target[:, i])
                if self.weight is not None:
                    assert self.weight.shape[0] == target.shape[1], \
                        'Expect weight shape [{}], get[{}]'.format(target.shape[1], self.weight.shape[0])
                    dice_loss *= self.weights[i]
                total_loss += dice_loss

        return total_loss/target.shape[1]