import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.autograd import Function

class DiceCoeff(Function):

    """Dice coeff for individual examples"""

    def forward(self, input, target):

        self.save_for_backward(input, target)

        eps = 0.0001

        self.inter = torch.dot(input.contiguous().view(-1), target.contiguous().view(-1))

        self.union = torch.sum(input) + torch.sum(target) + eps

        t = (2 * self.inter.float() + eps) / self.union.float()

        return t

    # This function has only a single output, so it gets only one gradient

    def backward(self, grad_output):

        input, target = self.saved_variables

        grad_input = grad_target = None

        if self.needs_input_grad[0]:

            grad_input = grad_output * 2 * (target * self.union - self.inter) \

                         / (self.union * self.union)

        if self.needs_input_grad[1]:

            grad_target = None

        return grad_input, grad_target

def dice_coeff(input, target):

    """Dice coeff for batches"""

    if input.is_cuda:

        s = torch.FloatTensor(1).cuda().zero_()

    else:

        s = torch.FloatTensor(1).zero_()

    for i, c in enumerate(zip(input, target)):

        s = s + DiceCoeff().forward(c[0], c[1])

    return s / (i + 1)

def DICESEN_loss(input, target):

    smooth = 0.00000001

    y_true_f = input.view(-1)

    y_pred_f = target.view(-1)

    intersection = torch.sum(torch.mul(y_true_f,y_pred_f))

    dice= (2. * intersection ) / (torch.mul(y_true_f,y_true_f).sum() + torch.mul(y_pred_f,y_pred_f).sum() + smooth)

    sen = (1. * intersection ) / (torch.mul(y_true_f,y_true_f).sum() + smooth)

    return 2-dice-sen   

class DiceSensitivityLoss(nn.Module):

    def __init__(self, n_classes):

        self.n_classes = n_classes

        super(DiceSensitivityLoss, self).__init__()

    def forward(self, inputs, targets, smooth = 1.):

        if self.n_classes == 1:

            inputs = torch.sigmoid(inputs)

        else:

            inputs = F.softmax(inputs, dim=1)

        y_true_f = inputs.view(-1)

        y_pred_f = targets.view(-1)

        intersection = (y_true_f * y_pred_f).sum()

        dice= (2. * intersection + smooth) / (y_pred_f.sum() + y_true_f.sum() + smooth)

        sen = (1. * intersection ) / (torch.mul(y_true_f,y_true_f).sum() + smooth)

        return 2 - dice-sen