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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
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