"""
Pytorch Implementation of thr focal loss taken from
https://github.com/clcarwin/focal_loss_pytorch/blob/master/focalloss.py
Credits : https://github.com/clcarwin

Only modified to add the option to ignore a label
"""

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


class FocalLoss(nn.Module):
    def __init__(self, gamma=0, alpha=None, size_average=True, ignore_label=None):
        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
        self.ignore_label = ignore_label

    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)
        if input.squeeze(1).dim() == 1:
            logpt = torch.sigmoid(input)
            logpt = logpt.view(-1)
        else:
            logpt = F.log_softmax(input, dim=-1)
            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.ignore_label is not None:
            loss = loss[target[:,0] != self.ignore_label]
        if self.size_average:
            return loss.mean()
        else:
            return loss.sum()
