loss.py

import torch
import torch.nn.functional as F
import torch.nn as nn
# Recommend
class CrossEntropyLoss2d(nn.Module):
    def __init__(self, weight=None, size_average=True):
        super(CrossEntropyLoss2d, self).__init__()
        self.nll_loss = nn.NLLLoss2d(weight, size_average)

    def forward(self, inputs, targets):
        return self.nll_loss(F.log_softmax(inputs), targets)

# this may be unstable sometimes.Notice set the size_average
def CrossEntropy2d(input, target, weight=None, size_average=False):
    # input:(n, c, h, w) target:(n, h, w)
    n, c, h, w = input.size()

    input = input.transpose(1, 2).transpose(2, 3).contiguous()
    input = input[target.view(n, h, w, 1).repeat(1, 1, 1, c) >= 0].view(-1, c)

    target_mask = target >= 0
    target = target[target_mask]
    #loss = F.nll_loss(F.log_softmax(input), target, weight=weight, size_average=False)
    loss = F.cross_entropy(input, target, weight=weight, size_average=False)
    if size_average:
        loss /= target_mask.sum().data[0]

    return loss