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


#https://github.com/clcarwin/focal_loss_pytorch/blob/master/focalloss.py
class FocalLoss(nn.Module):
    def __init__(self, gamma=0, alpha=None, size_average=True):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        if isinstance(alpha, (float, int)):
            self.alpha = torch.Tensor([alpha, 1 - alpha])
        if isinstance(alpha, list):
            self.alpha = torch.Tensor(alpha)
        self.size_average = size_average

    def forward(self, input, target):
        if input.dim() > 2:
            input = input.view(input.size(0), input.size(1), -1)  # N,C,H,W => N,C,H*W
            input = input.transpose(1, 2)    # N,C,H*W => N,H*W,C
            input = input.contiguous().view(-1, input.size(2))   # N,H*W,C => N*H*W,C
        target = target.view(-1, 1)
        logpt = F.log_softmax(input)
        logpt = logpt.gather(1, target)
        logpt = logpt.view(-1)
        pt = Variable(logpt.data.exp())

        if self.alpha is not None:
            if self.alpha.type() != input.data.type():
                self.alpha = self.alpha.type_as(input.data)
            at = self.alpha.gather(0, target.data.view(-1))
            logpt = logpt * Variable(at)

        loss = -1 * (1 - pt)**self.gamma * logpt
        if self.size_average:
            return loss.mean()
        else:
            return loss.sum()


class BinaryFocalLoss(nn.Module):
    def __init__(self, weight=None, gamma=2., alpha=1, reduction='mean'):
        nn.Module.__init__(self)
        self.weight = weight
        self.gamma = gamma
        self.reduction = reduction
        self.alpha = alpha
    def forward(self, inputs, targets):
        inputs = inputs.float()
        targets = targets.float()
        BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=self.reduction)
        pt = torch.exp(-BCE_loss) # prevents nans when probability 0
        F_loss = self.alpha * (1 - pt) ** self.gamma * BCE_loss
        return F_loss.mean()
