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Running
on
Zero
File size: 2,316 Bytes
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#!/usr/bin/python
# -*- encoding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class OhemCELoss(nn.Module):
def __init__(self, thresh, n_min, ignore_lb=255, *args, **kwargs):
super(OhemCELoss, self).__init__()
self.thresh = -torch.log(torch.tensor(thresh, dtype=torch.float)).cuda()
self.n_min = n_min
self.ignore_lb = ignore_lb
self.criteria = nn.CrossEntropyLoss(ignore_index=ignore_lb, reduction='none')
def forward(self, logits, labels):
N, C, H, W = logits.size()
loss = self.criteria(logits, labels).view(-1)
loss, _ = torch.sort(loss, descending=True)
if loss[self.n_min] > self.thresh:
loss = loss[loss>self.thresh]
else:
loss = loss[:self.n_min]
return torch.mean(loss)
class SoftmaxFocalLoss(nn.Module):
def __init__(self, gamma, ignore_lb=255, *args, **kwargs):
super(SoftmaxFocalLoss, self).__init__()
self.gamma = gamma
self.nll = nn.NLLLoss(ignore_index=ignore_lb)
def forward(self, logits, labels):
scores = F.softmax(logits, dim=1)
factor = torch.pow(1.-scores, self.gamma)
log_score = F.log_softmax(logits, dim=1)
log_score = factor * log_score
loss = self.nll(log_score, labels)
return loss
if __name__ == '__main__':
torch.manual_seed(15)
criteria1 = OhemCELoss(thresh=0.7, n_min=16*20*20//16).cuda()
criteria2 = OhemCELoss(thresh=0.7, n_min=16*20*20//16).cuda()
net1 = nn.Sequential(
nn.Conv2d(3, 19, kernel_size=3, stride=2, padding=1),
)
net1.cuda()
net1.train()
net2 = nn.Sequential(
nn.Conv2d(3, 19, kernel_size=3, stride=2, padding=1),
)
net2.cuda()
net2.train()
with torch.no_grad():
inten = torch.randn(16, 3, 20, 20).cuda()
lbs = torch.randint(0, 19, [16, 20, 20]).cuda()
lbs[1, :, :] = 255
logits1 = net1(inten)
logits1 = F.interpolate(logits1, inten.size()[2:], mode='bilinear')
logits2 = net2(inten)
logits2 = F.interpolate(logits2, inten.size()[2:], mode='bilinear')
loss1 = criteria1(logits1, lbs)
loss2 = criteria2(logits2, lbs)
loss = loss1 + loss2
print(loss.detach().cpu())
loss.backward()
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