utils/loss_function.py

import torch as t
import torch.nn as nn
#import numpy as np


class SaliencyLoss(nn.Module):
    def __init__(self):
        super(SaliencyLoss, self).__init__()

    def forward(self, preds, labels, loss_type='cc'):
        losses = []
        if loss_type == 'cc':
            for i in range(labels.shape[0]): # labels.shape[0] is batch size
                loss = loss_CC(preds[i],labels[i])
                losses.append(loss)

        elif loss_type == 'kldiv':
            for i in range(labels.shape[0]):
                loss = loss_KLdiv(preds[i],labels[i])
                losses.append(loss)

        elif loss_type == 'sim':
            for i in range(labels.shape[0]):
                loss = loss_similarity(preds[i],labels[i])
                losses.append(loss)

        elif loss_type == 'nss':
            for i in range(labels.shape[0]):
                loss = loss_NSS(preds[i],labels[i])
                losses.append(loss)
            
        return t.stack(losses).mean(dim=0, keepdim=True)
        
        
def loss_KLdiv(pred_map, gt_map):
    eps = 2.2204e-16
    pred_map = pred_map/t.sum(pred_map)
    gt_map = gt_map/t.sum(gt_map)
    div = t.sum(t.mul(gt_map, t.log(eps + t.div(gt_map,pred_map+eps))))
    return div 
        
    
def loss_CC(pred_map,gt_map):
    gt_map_ = (gt_map - t.mean(gt_map))
    pred_map_ = (pred_map - t.mean(pred_map))
    cc = t.sum(t.mul(gt_map_,pred_map_))/t.sqrt(t.sum(t.mul(gt_map_,gt_map_))*t.sum(t.mul(pred_map_,pred_map_)))
    return cc


def loss_similarity(pred_map,gt_map):
    gt_map = (gt_map - t.min(gt_map))/(t.max(gt_map)-t.min(gt_map))
    gt_map = gt_map/t.sum(gt_map)
    
    pred_map = (pred_map - t.min(pred_map))/(t.max(pred_map)-t.min(pred_map))
    pred_map = pred_map/t.sum(pred_map)
    
    diff = t.min(gt_map,pred_map)
    score = t.sum(diff)
    
    return score
    
    
def loss_NSS(pred_map,fix_map):
    '''ground truth here is fixation map'''

    pred_map_ = (pred_map - t.mean(pred_map))/t.std(pred_map)
    mask = fix_map.gt(0)
    score = t.mean(t.masked_select(pred_map_, mask))
    return score