I build a pytorch model based on conv1d. I gone through quantization and implemented some cases as well but all those are working on conv2d, bn,relu but In my case, my model is built on conv1d and PReLU. Does this quatization valid for these network layers? Because when I did quantization only the layers which are included in mapping is only quantized. Let me show you those layers for which quantization is valid(i.e which are included in mapping)
Please find the list of modules it supports:(according to source code in went through)
(Actual layer : quantized layer)
nn.Linear: nnq.Linear,
nn.ReLU: nnq.ReLU,
nn.ReLU6: nnq.ReLU6,
nn.Conv2d: nnq.Conv2d,
nn.Conv3d: nnq.Conv3d,
nn.BatchNorm2d: nnq.BatchNorm2d,
nn.BatchNorm3d: nnq.BatchNorm3d,
QuantStub: nnq.Quantize,
DeQuantStub: nnq.DeQuantize,
Wrapper Modules:
nnq.FloatFunctional: nnq.QFunctional,
Intrinsic modules:
nni.ConvReLU2d: nniq.ConvReLU2d,
nni.ConvReLU3d: nniq.ConvReLU3d,
nni.LinearReLU: nniq.LinearReLU,
nniqat.ConvReLU2d: nniq.ConvReLU2d,
nniqat.LinearReLU: nniq.LinearReLU,
nniqat.ConvBn2d: nnq.Conv2d,
nniqat.ConvBnReLU2d: nniq.ConvReLU2d,
QAT modules:
nnqat.Linear: nnq.Linear,
nnqat.Conv2d: nnq.Conv2d,
Is it, it means that quantization can’t be done on conv1d and PReLU?