INNER CODE UNIT · Python

forward

nv-tlabs/GSCNN · loss.py:100

    def forward(self, inputs, targets):
        segin, edgein = inputs
        segmask, edgemask = targets

        losses = {}

        losses['seg_loss'] = self.seg_weight * self.seg_loss(segin, segmask)
        losses['edge_loss'] = self.edge_weight * 20 * self.bce2d(edgein, edgemask)
        losses['att_loss'] = self.att_weight * self.edge_attention(segin, segmask, edgein)
        losses['dual_loss'] = self.dual_weight * self.dual_task(segin, segmask)
              
        return losses

#Img Weighted Loss
class ImageBasedCrossEntropyLoss2d(nn.Module):

    def __init__(self, classes, weight=None, size_average=True, ignore_index=255,
                 norm=False, upper_bound=1.0):

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