mirror of https://github.com/kritiksoman/GIMP-ML
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
#!/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()
|