Expected more than 1 value per channel when training, got input size torch.Size([1, 9, 1, 1])

ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 9, 1, 1])
原代碼：

import torch
import torch.nn as nn
model = nn.Sequential(
            nn.Conv2d(3, 9, 1, 1, 0, bias=False),
            nn.BatchNorm2d(9),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.BatchNorm2d(9)
        )
        
x = torch.rand([1, 3, 224, 224])
for i in range(len(model)):
    x = model[i](x)
    print(i,x.shape)

報錯：

Traceback (most recent call last):
  File "/home/jim/.local/lib/python3.5/site-packages/torch/nn/functional.py", line 1693, in batch_norm
0 torch.Size([1, 9, 224, 224])
1 torch.Size([1, 9, 224, 224])
2 torch.Size([1, 9, 224, 224])
3 torch.Size([1, 9, 1, 1])
    raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 9, 1, 1])

發現問題出在最後一層 nn.BatchNorm2d(9)，輸入數據的 batch 必須大於 1.
解決辦法：
將x = torch.rand([1, 3, 224, 224])
更改爲x = torch.rand([2, 3, 224, 224])

得到正常輸出

0 torch.Size([2, 9, 224, 224])
1 torch.Size([2, 9, 224, 224])
2 torch.Size([2, 9, 224, 224])
3 torch.Size([2, 9, 1, 1])
4 torch.Size([2, 9, 1, 1])

另外，如果不用自適應池化，且輸入的batch仍設爲1：

import torch
import torch.nn as nn
model = nn.Sequential(
            nn.Conv2d(3, 9, 1, 1, 0, bias=False),
            nn.BatchNorm2d(9),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(9)
        )
  
  x = torch.rand([1, 3, 224, 224])
for i in range(len(model)):
    x = model[i](x)
    print(i,x.shape)

也不會報錯

0 torch.Size([1, 9, 224, 224])
1 torch.Size([1, 9, 224, 224])
2 torch.Size([1, 9, 224, 224])
3 torch.Size([1, 9, 224, 224])

自適應平均池化後將224*224變爲1*1，說明batch爲1時，如果輸入爲1*1的特徵圖，則，不能正常通過 nn.BatchNorm2d。