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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
"""
@Author : Peike Li
@Contact : [email protected]
@File : kl_loss.py
@Time : 7/23/19 4:02 PM
@Desc :
@License : This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
import torch.nn.functional as F
from torch import nn
from datasets.target_generation import generate_edge_tensor
class ConsistencyLoss(nn.Module):
def __init__(self, ignore_index=255):
super(ConsistencyLoss, self).__init__()
self.ignore_index=ignore_index
def forward(self, parsing, edge, label):
parsing_pre = torch.argmax(parsing, dim=1)
parsing_pre[label==self.ignore_index]=self.ignore_index
generated_edge = generate_edge_tensor(parsing_pre)
edge_pre = torch.argmax(edge, dim=1)
v_generate_edge = generated_edge[label!=255]
v_edge_pre = edge_pre[label!=255]
v_edge_pre = v_edge_pre.type(torch.cuda.FloatTensor)
positive_union = (v_generate_edge==1)&(v_edge_pre==1) # only the positive values count
return F.smooth_l1_loss(v_generate_edge[positive_union].squeeze(0), v_edge_pre[positive_union].squeeze(0))
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