I have been experimenting with the post static quantization feature on VGG-16.
I know that
torch.quantization.convert() will automatically remap every layer in the model to its quantized implementation.
However, i noticed that, a few types of layer is not converted, which is:
nn.Dropout() should not be an issue, whether its quantized or not.
However, i am not sure if
nn.AdaptiveAvgPool2d() would do any difference if it is not quantized.
I have seen
nn.quantized.MaxPool2d() being mentioned here and tried to remap my layer to this module. But, it seems like it is still referring to
nn.modules.pooling.MaxPool2d() when i check the layer type after reassigning.
I have also seen
nn.quantized.functional.AdaptiveAvgPool2d() being mentioned in the Quantization documentation. But i have read from the forum, and found that, it is not conventional to directly call
functional, instead, its
module or its wrapper class should be called.
So, i would like to ask, is there any effect to my quantized model performance if i don’t change the
nn.AdaptiveAvgPool2d() to their quantized version?
Should i just leave
nn.AdaptiveAvgPool2d() as it is?
Or, if i should change to their quantized implementation, how should i do it?