How to set parameters for BN layers during finetuning?

When finetuning model from a pretrained one, the CAFFE users would set the BN layers with “use_global_stats: true”, which will employ the mean and std values in the pretrained model for the finetuning stage. In my works, I find that setting is sometimes important for performance. What should I do when using Pytorch if I want to employ the already-learned mean and std rather than the moving average in finetuning jobs?


for m in model.modules():
  if isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d):

# Run your training here

You can further set requires_grad=False for your BN layers, but that only effects weights + biases.

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