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import torch
import torch.nn as nn
import torch.nn.functional as F
from .constants import weights as constant_weights
class CrossEntropy2d(nn.Module):
def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs):
"""
weight (Tensor, optional): a manual rescaling weight given to each class.
If given, has to be a Tensor of size "nclasses"
"""
super(CrossEntropy2d, self).__init__()
self.reduction = reduction
self.ignore_label = ignore_label
self.weights = weights
if self.weights is not None:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.weights = torch.FloatTensor(constant_weights[weights]).to(device)
def forward(self, predict, target):
"""
Args:
predict:(n, c, h, w)
target:(n, 1, h, w)
"""
target = target.long()
assert not target.requires_grad
assert predict.dim() == 4, "{0}".format(predict.size())
assert target.dim() == 4, "{0}".format(target.size())
assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
assert target.size(1) == 1, "{0}".format(target.size(1))
assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2))
assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3))
target = target.squeeze(1)
n, c, h, w = predict.size()
target_mask = (target >= 0) * (target != self.ignore_label)
target = target[target_mask]
predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction)
return loss
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