Comments (3)
It looks like you trained the teacher model with DoReFa quantization. Resuming from quantization aware training is still an open issue, for which there is a workaround you can apply - see here (Note that in the link WRPNQuantizer
is modified, you'll need to modify DorefaQuantizer
).
But - Is there a specific reason you'd want to have a quantized model as the teacher? An FP32 baseline would at least as good.
Regarding your question on teacher/student depth - there are not requirements on the teacher model. Having said that, the purpose of knowledge distillation is to use a model with bigger representational capacity to help train a model with smaller capacity. So using a shallower teacher goes against that purpose.
If you do use a shallower teacher, I guess it might help in the early stages of training to "point" the student in the right direction. But at some point you'd expect the deeper student model to surpass the performance of the shallower teacher model, at which point it doesn't make sense to continue with the distillation. In any case, I haven't tried it myself, and assuming you don't have any specific restrictions, I don't see a reason to do it.
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@guyjacob
HI,your answer really helps.I use fp32 to train resnet56 as teacher and resnet20 as student.It works.
Thank you ^.^
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You're welcome!
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Related Issues (20)
- Could you provide the checkpoints for structural pruning experiments?
- checkpoints example in example jupyter-notebook download denied. HOT 1
- Support for PyTorch 1.7? HOT 3
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- Higher than 8-bit Quantization not working properly!?
- yolo4 custom object detection deep compression
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- --load-serialized will make model fail to prune HOT 1
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- Sensitivity Analysis
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