INNER CODE UNIT · Python

ImageBasedCrossEntropyLoss2d

nv-tlabs/GSCNN · loss.py:114

class ImageBasedCrossEntropyLoss2d(nn.Module):

    def __init__(self, classes, weight=None, size_average=True, ignore_index=255,
                 norm=False, upper_bound=1.0):
        super(ImageBasedCrossEntropyLoss2d, self).__init__()
        logging.info("Using Per Image based weighted loss")
        self.num_classes = classes
        self.nll_loss = nn.NLLLoss2d(weight, size_average, ignore_index)
        self.norm = norm
        self.upper_bound = upper_bound
        self.batch_weights = cfg.BATCH_WEIGHTING

    def calculateWeights(self, target):
        hist = np.histogram(target.flatten(), range(
            self.num_classes + 1), normed=True)[0]
        if self.norm:
            hist = ((hist != 0) * self.upper_bound * (1 / hist)) + 1
        else:

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