loss.py 文件源码

def forward(self, input, target, save=True):
        if save:
            self.save_for_backward(input, target)
        eps = 0.000001
        _, result_ = input.max(1)
        result_ = torch.squeeze(result_)
        if input.is_cuda:
            result = torch.cuda.FloatTensor(result_.size())
            self.target_ = torch.cuda.FloatTensor(target.size())
        else:
            result = torch.FloatTensor(result_.size())
            self.target_ = torch.FloatTensor(target.size())
        result.copy_(result_)
        self.target_.copy_(target)
        target = self.target_
#       print(input)
        intersect = torch.dot(result, target)
        # binary values so sum the same as sum of squares
        result_sum = torch.sum(result)
        target_sum = torch.sum(target)
        union = result_sum + target_sum + (2*eps)

        # the target volume can be empty - so we still want to
        # end up with a score of 1 if the result is 0/0
        IoU = intersect / union
        print('union: {:.3f}\t intersect: {:.6f}\t target_sum: {:.0f} IoU: result_sum: {:.0f} IoU {:.7f}'.format(
            union, intersect, target_sum, result_sum, 2*IoU))
        out = torch.FloatTensor(1).fill_(2*IoU)
        self.intersect, self.union = intersect, union
        return out