I actually first thought that this might be a problem, but when I first searched for this specific error, I found this issue. With respect to the mentioned issue, this is just probably due to the fact the InvertedResidual block will follow one of two flows for calculation, and this warning is probably due to that.
I mean, when I print the model, it seems that the layers are properly quantized (not sure about this though since this is essentially my first quantized model and I don’t know what to expect). The print output is:
NewModel(
  (quant): Quantize(scale=tensor([0.0374]), zero_point=tensor([57]), dtype=torch.quint8)
  (model): MobileNetV2(
    (features): Sequential(
      (0): ConvBNActivation(
        (0): QuantizedConv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), scale=0.03452397137880325, zero_point=58, padding=(1, 1), bias=False)
        (1): QuantizedBatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): QuantizedReLU6(inplace=True)
      )
      (1): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.008563793264329433, zero_point=52, padding=(1, 1), groups=32, bias=False)
            (1): QuantizedBatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): QuantizedConv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), scale=0.008242965675890446, zero_point=79, bias=False)
          (2): QuantizedBatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (2): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), scale=0.004633823875337839, zero_point=58, bias=False)
            (1): QuantizedBatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), scale=0.0007947214762680233, zero_point=75, padding=(1, 1), groups=96, bias=False)
            (1): QuantizedBatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), scale=0.0007379651651717722, zero_point=64, bias=False)
          (3): QuantizedBatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (3): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), scale=0.0004368616209831089, zero_point=63, bias=False)
            (1): QuantizedBatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), scale=6.097953882999718e-05, zero_point=67, padding=(1, 1), groups=144, bias=False)
            (1): QuantizedBatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), scale=8.161034202203155e-05, zero_point=62, bias=False)
          (3): QuantizedBatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=0.0007560973172076046, zero_point=64
          (activation_post_process): Identity()
        )
      )
      (4): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), scale=0.00046657773782499135, zero_point=65, bias=False)
            (1): QuantizedBatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), scale=6.993811985012144e-05, zero_point=67, padding=(1, 1), groups=144, bias=False)
            (1): QuantizedBatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), scale=6.939686136320233e-05, zero_point=69, bias=False)
          (3): QuantizedBatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (5): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), scale=5.0384467613184825e-05, zero_point=58, bias=False)
            (1): QuantizedBatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), scale=4.988682576367864e-06, zero_point=65, padding=(1, 1), groups=192, bias=False)
            (1): QuantizedBatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), scale=4.922353127767565e-06, zero_point=70, bias=False)
          (3): QuantizedBatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=7.048285624478012e-05, zero_point=68
          (activation_post_process): Identity()
        )
      )
      (6): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), scale=3.9113976527005434e-05, zero_point=64, bias=False)
            (1): QuantizedBatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), scale=4.21840013586916e-06, zero_point=59, padding=(1, 1), groups=192, bias=False)
            (1): QuantizedBatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), scale=6.124475930846529e-06, zero_point=69, bias=False)
          (3): QuantizedBatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=6.926279456820339e-05, zero_point=68
          (activation_post_process): Identity()
        )
      )
      (7): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), scale=4.882919165538624e-05, zero_point=58, bias=False)
            (1): QuantizedBatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), scale=6.18789727013791e-06, zero_point=65, padding=(1, 1), groups=192, bias=False)
            (1): QuantizedBatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), scale=4.26427686761599e-06, zero_point=69, bias=False)
          (3): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (8): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), scale=2.777898998829187e-06, zero_point=66, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), scale=2.495579565220396e-07, zero_point=74, padding=(1, 1), groups=384, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), scale=2.758661423740705e-07, zero_point=70, bias=False)
          (3): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=4.288005584385246e-06, zero_point=69
          (activation_post_process): Identity()
        )
      )
      (9): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), scale=3.1199779186863452e-06, zero_point=65, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), scale=2.582654303751042e-07, zero_point=80, padding=(1, 1), groups=384, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), scale=2.4579065893703955e-07, zero_point=68, bias=False)
          (3): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=4.267273652658332e-06, zero_point=69
          (activation_post_process): Identity()
        )
      )
      (10): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), scale=2.7751552806876134e-06, zero_point=67, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), scale=2.0391455279877846e-07, zero_point=71, padding=(1, 1), groups=384, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), scale=2.1148737516796245e-07, zero_point=59, bias=False)
          (3): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=4.199701834295411e-06, zero_point=70
          (activation_post_process): Identity()
        )
      )
      (11): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), scale=2.6447246455063578e-06, zero_point=64, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), scale=2.6378887696409947e-07, zero_point=79, padding=(1, 1), groups=384, bias=False)
            (1): QuantizedBatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), scale=2.007121366887077e-07, zero_point=70, bias=False)
          (3): QuantizedBatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (12): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=57, bias=False)
            (1): QuantizedBatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=5, padding=(1, 1), groups=576, bias=False)
            (1): QuantizedBatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=6, bias=False)
          (3): QuantizedBatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.9502708425989113e-07, zero_point=72
          (activation_post_process): Identity()
        )
      )
      (13): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.2644522939808667e-07, zero_point=64, bias=False)
            (1): QuantizedBatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=6, padding=(1, 1), groups=576, bias=False)
            (1): QuantizedBatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=6, bias=False)
          (3): QuantizedBatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.9090400371624128e-07, zero_point=72
          (activation_post_process): Identity()
        )
      )
      (14): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), scale=1.250595147439526e-07, zero_point=60, bias=False)
            (1): QuantizedBatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), scale=1.1920928955078125e-07, zero_point=6, padding=(1, 1), groups=576, bias=False)
            (1): QuantizedBatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=5, bias=False)
          (3): QuantizedBatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (15): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=3, bias=False)
            (1): QuantizedBatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, padding=(1, 1), groups=960, bias=False)
            (1): QuantizedBatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, bias=False)
          (3): QuantizedBatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.1920928955078125e-07, zero_point=5
          (activation_post_process): Identity()
        )
      )
      (16): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=3, bias=False)
            (1): QuantizedBatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, padding=(1, 1), groups=960, bias=False)
            (1): QuantizedBatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, bias=False)
          (3): QuantizedBatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.1920928955078125e-07, zero_point=5
          (activation_post_process): Identity()
        )
      )
      (17): InvertedResidual(
        (conv): Sequential(
          (0): ConvBNActivation(
            (0): QuantizedConv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=3, bias=False)
            (1): QuantizedBatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (1): ConvBNActivation(
            (0): QuantizedConv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, padding=(1, 1), groups=960, bias=False)
            (1): QuantizedBatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (2): QuantizedReLU6(inplace=True)
          )
          (2): QuantizedConv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, bias=False)
          (3): QuantizedBatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (floatFunctional): QFunctional(
          scale=1.0, zero_point=0
          (activation_post_process): Identity()
        )
      )
      (18): ConvBNActivation(
        (0): QuantizedConv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), scale=1.1920928955078125e-07, zero_point=0, bias=False)
        (1): QuantizedBatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): QuantizedReLU6(inplace=True)
      )
    )
    (classifier): Sequential(
      (0): Dropout(p=0.2, inplace=False)
      (1): QuantizedLinear(in_features=1280, out_features=1000, scale=1.1920928955078125e-07, zero_point=0, qscheme=torch.per_channel_affine)
    )
  )
  (dequant): DeQuantize()
  (softmax): Softmax(dim=None)
)