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if not, how can i make it work for CNN with groupnorm?
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@yxchng basically you can refer to what we do on BatchNorm: https://github.com/keyu-tian/SparK/blob/main/pretrain/encoder.py#L26. Theoretically speaking, only unmasked areas should be taken into account when calculating the mean and std for the normalization layer. You may mimic writing a SparseGroupNorm like our SparseBatchNorm2d.
SparseGroupNorm
SparseBatchNorm2d
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if not, how can i make it work for CNN with groupnorm?
The text was updated successfully, but these errors were encountered: