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Bug in JointAutoregressiveHierarchicalPriors._compress_ar
See original GitHub issuehttps://github.com/InterDigitalInc/CompressAI/blob/master/compressai/models/priors.py
In JointAutoregressiveHierarchicalPriors._compress_ar:
ctx_p = F.conv2d( y_crop, self.context_prediction.weight, bias=self.context_prediction.bias )
self.context_prediction.weight ignores the mask, leading to the context mismatch between _compress_ar and _decompress_ar.
y_hat in _compress_ar is the full feature and needs to be masked.
Solution:
masked_weight = self.context_prediction.weight * self.context_prediction.mask
ctx_p = F.conv2d( y_crop, masked_weight, bias=self.context_prediction.bias )
Issue Analytics
- State:
- Created 3 years ago
- Comments:11 (5 by maintainers)
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Thanks for the code, I managed to re-create the bug! What’s happening is you are saving your model before evaluating it, so the weights have been overwritten by the optimizer. I’ll apply a fix with your earlier proposal. Thanks for reporting this!
I hope the information helps. It is not a big problem for the program anyway.