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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 | |