torch.nn.SmoothL1Loss()和smooth_l1_loss()怎么使用

这篇文章主要介绍"torch.nn.SmoothL1Loss()和smooth_l1_loss()怎么使用"，在日常操作中，相信很多人在torch.nn.SmoothL1Loss()和smooth_l1_loss()怎么使用问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答"torch.nn.SmoothL1Loss()和smooth_l1_loss()怎么使用"的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

Microsoft Windows [版本 10.0.18363.1256](c) 2019 Microsoft Corporation。保留所有权利。C:\Users\chenxuqi>conda activate ssd4pytorch2_2_0(ssd4pytorch2_2_0) C:\Users\chenxuqi>pythonPython 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32Type "help", "copyright", "credits" or "license" for more information.>>> import torch>>> import torch.nn.functional as F>>> input = torch.zeros(2,3)>>> inputtensor([[0., 0., 0.],[0., 0., 0.]])>>> target = torch.tensor([[0.5,1.2,0.8],[0.4,1.5,2.2]])>>> targettensor([[0.5000, 1.2000, 0.8000],[0.4000, 1.5000, 2.2000]])>>>>>> F.smooth_l1_loss(target, input, size_average=False)D:\Anaconda3\envs\ssd4pytorch2_2_0\lib\site-packages\torch\nn\_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.  warnings.warn(warning.format(ret))tensor(3.9250)>>> F.smooth_l1_loss(target, input, size_average=True)D:\Anaconda3\envs\ssd4pytorch2_2_0\lib\site-packages\torch\nn\_reduction.py:43: UserWarning: size_average and reduce args will be deprecated, please use reduction='mean' instead.  warnings.warn(warning.format(ret))tensor(0.6542)>>> 3.9250/6.00.6541666666666667>>>>>>>>>>>> F.smooth_l1_loss(target, input, reduction = 'sum')tensor(3.9250)>>>>>> F.smooth_l1_loss(target, input, reduction='mean')tensor(0.6542)>>>>>> F.smooth_l1_loss(target, input, reduction='none')tensor([[0.1250, 0.7000, 0.3200],[0.0800, 1.0000, 1.7000]])>>>>>> inputtensor([[0., 0., 0.],[0., 0., 0.]])>>> targettensor([[0.5000, 1.2000, 0.8000],[0.4000, 1.5000, 2.2000]])>>>>>>>>>

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