47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
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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from torch.autograd import Variable
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def _assert_no_grad(variable):
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assert not variable.requires_grad, \
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"nn criterions don't compute the gradient w.r.t. targets - please " \
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"mark these variables as volatile or not requiring gradients"
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class _Loss(nn.Module):
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def __init__(self, size_average=True):
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super(_Loss, self).__init__()
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self.size_average = size_average
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class _WeightedLoss(_Loss):
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def __init__(self, weight=None, size_average=True):
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super(_WeightedLoss, self).__init__(size_average)
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self.register_buffer('weight', weight)
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class EMLossForTarget(_WeightedLoss):
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def __init__(self, weight=None, size_average=True, ignore_index=-100, reduce=True, nClass=10):
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super(EMLossForTarget, self).__init__(weight, size_average)
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self.nClass = nClass
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def forward(self, input):
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batch_size = input.size(0)
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prob = F.softmax(input, dim=1)
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# prob = F.sigmoid(input)
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prob_source = prob[:, :self.nClass]
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prob_target = prob[:, self.nClass:]
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prob_sum = prob_target + prob_source
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if (prob_sum.data.cpu() == 0).sum() != 0:
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weight_sum = torch.FloatTensor(batch_size, self.nClass).fill_(0)
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weight_sum[prob_sum.data.cpu() == 0] = 1e-6
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weight_sum = Variable(weight_sum).cuda()
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loss_sum = -(prob_sum + weight_sum).log().mul(prob_sum).sum(1).mean()
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else:
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loss_sum = -prob_sum.log().mul(prob_sum).sum(1).mean()
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return loss_sum
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