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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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