batchnorm handing issue when inference

[MagicSource]

When inference on single image (batch size = 1) got error:

mobilenetv3.py", line 199, in forward
    out = self.hs3(self.bn3(self.linear3(out)))
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/batchnorm.py", line 76, in forward
    exponential_average_factor, self.eps)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1619, in batch_norm
    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, 1280])

[zihaozhang9]

I had the same problem. How to solve this problem?

[CConory]

You can heed https://arxiv.org/pdf/1905.02244.pdf that there is no bn in the SEmodule.
When the AdaptiveAvgpool option make the feature_map to channelsX1X1 and the batch size is One, the batch normalization will wrong.
So just remove the BN option in the SEmodule will be ok!

[Hao]

Same question, interesting problem hah hah

[zihaozhang9]

我已收到您发送的邮件。我会及时查看。