mirror of
https://github.com/kritiksoman/GIMP-ML
synced 2024-11-06 03:20:34 +00:00
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
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#!/usr/bin/python
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# -*- encoding: utf-8 -*-
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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class OhemCELoss(nn.Module):
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def __init__(self, thresh, n_min, ignore_lb=255, *args, **kwargs):
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super(OhemCELoss, self).__init__()
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self.thresh = -torch.log(torch.tensor(thresh, dtype=torch.float)).cuda()
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self.n_min = n_min
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self.ignore_lb = ignore_lb
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self.criteria = nn.CrossEntropyLoss(ignore_index=ignore_lb, reduction='none')
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def forward(self, logits, labels):
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N, C, H, W = logits.size()
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loss = self.criteria(logits, labels).view(-1)
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loss, _ = torch.sort(loss, descending=True)
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if loss[self.n_min] > self.thresh:
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loss = loss[loss>self.thresh]
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else:
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loss = loss[:self.n_min]
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return torch.mean(loss)
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class SoftmaxFocalLoss(nn.Module):
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def __init__(self, gamma, ignore_lb=255, *args, **kwargs):
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super(SoftmaxFocalLoss, self).__init__()
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self.gamma = gamma
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self.nll = nn.NLLLoss(ignore_index=ignore_lb)
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def forward(self, logits, labels):
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scores = F.softmax(logits, dim=1)
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factor = torch.pow(1.-scores, self.gamma)
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log_score = F.log_softmax(logits, dim=1)
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log_score = factor * log_score
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loss = self.nll(log_score, labels)
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return loss
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if __name__ == '__main__':
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torch.manual_seed(15)
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criteria1 = OhemCELoss(thresh=0.7, n_min=16*20*20//16).cuda()
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criteria2 = OhemCELoss(thresh=0.7, n_min=16*20*20//16).cuda()
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net1 = nn.Sequential(
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nn.Conv2d(3, 19, kernel_size=3, stride=2, padding=1),
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)
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net1.cuda()
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net1.train()
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net2 = nn.Sequential(
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nn.Conv2d(3, 19, kernel_size=3, stride=2, padding=1),
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)
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net2.cuda()
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net2.train()
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with torch.no_grad():
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inten = torch.randn(16, 3, 20, 20).cuda()
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lbs = torch.randint(0, 19, [16, 20, 20]).cuda()
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lbs[1, :, :] = 255
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logits1 = net1(inten)
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logits1 = F.interpolate(logits1, inten.size()[2:], mode='bilinear')
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logits2 = net2(inten)
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logits2 = F.interpolate(logits2, inten.size()[2:], mode='bilinear')
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loss1 = criteria1(logits1, lbs)
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loss2 = criteria2(logits2, lbs)
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loss = loss1 + loss2
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print(loss.detach().cpu())
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loss.backward()
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