loss.py 文件源码

def dice_error(input, target):
    eps = 0.000001
    _, result_ = input.max(1)
    result_ = torch.squeeze(result_)
    if input.is_cuda:
        result = torch.cuda.FloatTensor(result_.size())
        target_ = torch.cuda.FloatTensor(target.size())
    else:
        result = torch.FloatTensor(result_.size())
        target_ = torch.FloatTensor(target.size())
    result.copy_(result_.data)
    target_.copy_(target.data)
    target = target_
    intersect = torch.dot(result, target)

    result_sum = torch.sum(result)
    target_sum = torch.sum(target)
    union = result_sum + target_sum + 2*eps
    intersect = np.max([eps, intersect])
    # 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))
    return 2*IoU