Hi @Vasiliy_Kuznetsov , hope you are fine.
I am facing the same issue, and there are some layers that are not being quantized, like
SiLU,Batchnorm1d etc.
Here you can see:
QuantizationModule(
  (model): Sequential(
    (0): Sequential(
      (0): QuantizedConv2d(3, 40, kernel_size=(3, 3), stride=(2, 2), scale=1.0, zero_point=0)
      (1): QuantizedBatchNorm2d(40, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      (2): SiLU(inplace=True)
      (3): Sequential(
        (0): Sequential(
          (0): DepthwiseSeparableConv(
            (conv_dw): QuantizedConv2d(40, 40, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=40)
            (bn1): QuantizedBatchNorm2d(40, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(40, 10, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(10, 40, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pw): QuantizedConv2d(40, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn2): QuantizedBatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): Identity()
          )
          (1): DepthwiseSeparableConv(
            (conv_dw): QuantizedConv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=24)
            (bn1): QuantizedBatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(24, 6, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(6, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pw): QuantizedConv2d(24, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn2): QuantizedBatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): Identity()
          )
        )
        (1): Sequential(
          (0): InvertedResidual(
            (conv_pw): QuantizedConv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): Conv2dSame(144, 144, kernel_size=(3, 3), stride=(2, 2), groups=144, bias=False)
            (bn2): QuantizedBatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(144, 6, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(6, 144, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (1): InvertedResidual(
            (conv_pw): QuantizedConv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=192)
            (bn2): QuantizedBatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(192, 8, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(8, 192, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): InvertedResidual(
            (conv_pw): QuantizedConv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=192)
            (bn2): QuantizedBatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(192, 8, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(8, 192, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (2): Sequential(
          (0): InvertedResidual(
            (conv_pw): QuantizedConv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): Conv2dSame(192, 192, kernel_size=(5, 5), stride=(2, 2), groups=192, bias=False)
            (bn2): QuantizedBatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(192, 8, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(8, 192, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (1): InvertedResidual(
            (conv_pw): QuantizedConv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(288, 288, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=288)
            (bn2): QuantizedBatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(288, 12, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(12, 288, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): InvertedResidual(
            (conv_pw): QuantizedConv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(288, 288, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=288)
            (bn2): QuantizedBatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(288, 12, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(12, 288, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (3): Sequential(
          (0): InvertedResidual(
            (conv_pw): QuantizedConv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): Conv2dSame(288, 288, kernel_size=(3, 3), stride=(2, 2), groups=288, bias=False)
            (bn2): QuantizedBatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(288, 12, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(12, 288, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (1): InvertedResidual(
            (conv_pw): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=576)
            (bn2): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(576, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(24, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): InvertedResidual(
            (conv_pw): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=576)
            (bn2): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(576, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(24, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (3): InvertedResidual(
            (conv_pw): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=576)
            (bn2): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(576, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(24, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (4): InvertedResidual(
            (conv_pw): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=576)
            (bn2): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(576, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(24, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (4): Sequential(
          (0): InvertedResidual(
            (conv_pw): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(576, 576, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=576)
            (bn2): QuantizedBatchNorm2d(576, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(576, 24, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(24, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(576, 136, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(136, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (1): InvertedResidual(
            (conv_pw): QuantizedConv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=816)
            (bn2): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(816, 34, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(34, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(136, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): InvertedResidual(
            (conv_pw): QuantizedConv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=816)
            (bn2): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(816, 34, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(34, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(136, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (3): InvertedResidual(
            (conv_pw): QuantizedConv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=816)
            (bn2): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(816, 34, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(34, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(136, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (4): InvertedResidual(
            (conv_pw): QuantizedConv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(816, 816, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=816)
            (bn2): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(816, 34, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(34, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(816, 136, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(136, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (5): Sequential(
          (0): InvertedResidual(
            (conv_pw): QuantizedConv2d(136, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): Conv2dSame(816, 816, kernel_size=(5, 5), stride=(2, 2), groups=816, bias=False)
            (bn2): QuantizedBatchNorm2d(816, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(816, 34, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(34, 816, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(816, 232, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(232, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (1): InvertedResidual(
            (conv_pw): QuantizedConv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=1392)
            (bn2): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(232, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (2): InvertedResidual(
            (conv_pw): QuantizedConv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=1392)
            (bn2): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(232, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (3): InvertedResidual(
            (conv_pw): QuantizedConv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=1392)
            (bn2): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(232, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (4): InvertedResidual(
            (conv_pw): QuantizedConv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=1392)
            (bn2): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(232, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (5): InvertedResidual(
            (conv_pw): QuantizedConv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(1392, 1392, kernel_size=(5, 5), stride=(1, 1), scale=1.0, zero_point=0, padding=(2, 2), groups=1392)
            (bn2): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(1392, 232, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(232, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (6): Sequential(
          (0): InvertedResidual(
            (conv_pw): QuantizedConv2d(232, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(1392, 1392, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=1392)
            (bn2): QuantizedBatchNorm2d(1392, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(1392, 58, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(58, 1392, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(1392, 384, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
          (1): InvertedResidual(
            (conv_pw): QuantizedConv2d(384, 2304, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn1): QuantizedBatchNorm2d(2304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act1): SiLU(inplace=True)
            (conv_dw): QuantizedConv2d(2304, 2304, kernel_size=(3, 3), stride=(1, 1), scale=1.0, zero_point=0, padding=(1, 1), groups=2304)
            (bn2): QuantizedBatchNorm2d(2304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
            (act2): SiLU(inplace=True)
            (se): SqueezeExcite(
              (conv_reduce): QuantizedConv2d(2304, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
              (act1): SiLU(inplace=True)
              (conv_expand): QuantizedConv2d(96, 2304, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            )
            (conv_pwl): QuantizedConv2d(2304, 384, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
            (bn3): QuantizedBatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (4): QuantizedConv2d(384, 1536, kernel_size=(1, 1), stride=(1, 1), scale=1.0, zero_point=0)
      (5): QuantizedBatchNorm2d(1536, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
      (6): SiLU(inplace=True)
    )
    (1): Sequential(
      (0): AdaptiveConcatPool2d(
        (ap): AdaptiveAvgPool2d(output_size=1)
        (mp): AdaptiveMaxPool2d(output_size=1)
      )
      (1): Flatten(full=False)
      (2): BatchNorm1d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): Dropout(p=0.25, inplace=False)
      (4): QuantizedLinear(in_features=3072, out_features=512, scale=1.0, zero_point=0, qscheme=torch.per_tensor_affine)
      (5): ReLU(inplace=True)
      (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (7): Dropout(p=0.5, inplace=False)
      (8): QuantizedLinear(in_features=512, out_features=73, scale=1.0, zero_point=0, qscheme=torch.per_tensor_affine)
    )
  )
  (quant): Quantize(scale=tensor([1.]), zero_point=tensor([0]), dtype=torch.quint8)
  (dequant): DeQuantize()
)

Now I can do filter out SiLU, by following:
for name, layer in model_static_quantized2.named_modules():
    if isinstance(layer, nn.SiLU):
        print(name, layer)

But, now how can I specifically quant, and dequant the layers in the model.
Thanks for any help…