x = Variable(torch.Tensor([[[1, 2, 1],
       [2, 2, 1],
       [0, 1, 1]],
       [[0, 1, 3],
       [2, 3, 1],
       [0, 0, 1]]]))

x = x.view([1, 2, 3, 3])
print("x输入", x)

y = Variable(torch.LongTensor([[1, 0, 1],
       [0, 0, 1],
       [1, 1, 1]]))

y = y.view([1, 3, 3])

loss = nn.NLLLoss2d()
out = loss(x, y)
print(out)

.
…
…
loss= -(-1.3133-0.3133-0.1269-0.6931-1.3133-0.6931-0.6931-1.3133-0.6931)/9 =0.7947222222222223

import torch
import torch.nn as nn
from torch import autograd
import torch.nn.functional as F
 
inputs_tensor = torch.FloatTensor([
[[2, 4],
 [1, 2]],
[[5, 3],
 [3, 0]],
[[5, 3],
 [5, 2]],
[[4, 2],
 [3, 2]],
 ])
inputs_tensor = torch.unsqueeze(inputs_tensor,0)
# inputs_tensor = torch.unsqueeze(inputs_tensor,1)
print '--input size(nBatch x nClasses x height x width): ', inputs_tensor.shape
 
targets_tensor = torch.LongTensor([
 [0, 2],
 [2, 3]
])
 
targets_tensor = torch.unsqueeze(targets_tensor,0)
print '--target size(nBatch x height x width): ', targets_tensor.shape
 
inputs_variable = autograd.Variable(inputs_tensor, requires_grad=True)
inputs_variable = F.log_softmax(inputs_variable)
targets_variable = autograd.Variable(targets_tensor)
 
loss = nn.NLLLoss2d()
output = loss(inputs_variable, targets_variable)
print '--NLLLoss2d: {}'.format(output)