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75 lines
2.4 KiB
75 lines
2.4 KiB
4 years ago
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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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class ParsingRelationLoss(nn.Module):
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def __init__(self):
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super(ParsingRelationLoss, self).__init__()
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def forward(self,logits):
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n,c,h,w = logits.shape
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loss_all = []
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for i in range(0,h-1):
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loss_all.append(logits[:,:,i,:] - logits[:,:,i+1,:])
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#loss0 : n,c,w
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loss = torch.cat(loss_all)
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return torch.nn.functional.smooth_l1_loss(loss,torch.zeros_like(loss))
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class ParsingRelationDis(nn.Module):
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def __init__(self):
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super(ParsingRelationDis, self).__init__()
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self.l1 = torch.nn.L1Loss()
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# self.l1 = torch.nn.MSELoss()
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def forward(self, x):
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n,dim,num_rows,num_cols = x.shape
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x = torch.nn.functional.softmax(x[:,:dim-1,:,:],dim=1)
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embedding = torch.Tensor(np.arange(dim-1)).float().to(x.device).view(1,-1,1,1)
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pos = torch.sum(x*embedding,dim = 1)
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diff_list1 = []
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for i in range(0,num_rows // 2):
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diff_list1.append(pos[:,i,:] - pos[:,i+1,:])
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loss = 0
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for i in range(len(diff_list1)-1):
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loss += self.l1(diff_list1[i],diff_list1[i+1])
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loss /= len(diff_list1) - 1
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return loss
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