为什么 PyTorch BatchNorm1D 在规范化整数类型张量时给出"batch_norm"未实现的"Long"错误？

Question

我正在尝试学习Pytorch框架中的一些函数，并且在规范化一个简单的整数张量时由于以下错误而被卡住。有人能帮我一下吗?

下面是重现错误的示例代码-

import torch
import torch.nn as nn
#Integer type tensor
test_int_input = torch.randint(size = [3,5],low=1,high=9)
# BatchNorm1D object
batchnorm1D = nn.BatchNorm1d(num_features=5)
test_output = batchnorm1D(test_int_input)

错误——

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-38-6c672cd731fa> in <module>
1 batchnorm1D = nn.BatchNorm1d(num_features=5)
----> 2 test_output = batchnorm1D(test_input)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530             result = self._slow_forward(*input, **kwargs)
531         else:
--> 532             result = self.forward(*input, **kwargs)
533         for hook in self._forward_hooks.values():
534             hook_result = hook(self, input, result)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/batchnorm.py in forward(self, input)
105             input, self.running_mean, self.running_var, self.weight, self.bias,
106             self.training or not self.track_running_stats,
--> 107             exponential_average_factor, self.eps)
108 
109 
/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
1668     return torch.batch_norm(
1669         input, weight, bias, running_mean, running_var,
-> 1670         training, momentum, eps, torch.backends.cudnn.enabled
1671     )
1672 
RuntimeError: "batch_norm" not implemented for 'Long'  

然而，如果我们尝试在不同的非int张量上应用相同的方法，那么它就可以工作。下面是示例-

import torch
import torch.nn as nn
#Integer type tensor
#test_input = torch.randn(size = [3,5])
# BatchNorm1D object
batchnorm1D = nn.BatchNorm1d(num_features=5)
test_output = batchnorm1D(test_input)
test_output

输出——

tensor([[ 0.4311, -1.1987,  0.9059,  1.1424,  1.2174],
[-1.3820,  1.2492, -1.3934,  0.1508,  0.0146],
[ 0.9509, -0.0505,  0.4875, -1.2931, -1.2320]],
grad_fn=<NativeBatchNormBackward>)

Answer 1

你的输入张量应该是一个浮点数:

>>> batchnorm1D(test_int_input.float())
tensor([[-5.9605e-08, -1.3887e+00, -9.8058e-01,  2.6726e-01,  1.4142e+00],
[-1.2247e+00,  4.6291e-01,  1.3728e+00, -1.3363e+00, -7.0711e-01],
[ 1.2247e+00,  9.2582e-01, -3.9223e-01,  1.0690e+00, -7.0711e-01]],
grad_fn=<NativeBatchNormBackward0>)