loss_function.py

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

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):
    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)
        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'
        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