Here is the output of print(fused_model):
FasterRCNN(
  (transform): GeneralizedRCNNTransform(
      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
      Resize(min_size=(320,), max_size=640, mode='bilinear')
  )
  (backbone): BackboneWithFPN(
    (body): IntermediateLayerGetter(
      (0): ConvBNActivation(
        (0): ConvBn2d(
          (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (1): Identity()
        (2): Hardswish()
      )
      (1): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=16, bias=False)
              (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (2): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (1): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=64, bias=False)
              (1): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (2): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(64, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (3): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (1): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
              (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (2): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (4): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (1): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(72, 72, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=72, bias=False)
              (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(72, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (5): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (1): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False)
              (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (6): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (1): ConvBNActivation(
            (0): ConvBnReLU2d(
              (0): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False)
              (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): Identity()
            (2): Identity()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(120, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (7): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)
              (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (8): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(80, 200, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(200, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(200, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=200, bias=False)
              (1): BatchNorm2d(200, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(200, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (9): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(80, 184, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(184, 184, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=184, bias=False)
              (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(184, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (10): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(80, 184, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(184, 184, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=184, bias=False)
              (1): BatchNorm2d(184, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(184, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (11): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(480, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)
              (1): BatchNorm2d(480, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(480, 120, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(120, 480, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(112, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (12): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=672, bias=False)
              (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(672, 168, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(168, 672, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(112, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (13): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)
              (1): BatchNorm2d(672, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(672, 168, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(168, 672, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(672, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (14): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)
              (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(960, 240, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(240, 960, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (15): QuantizableInvertedResidual(
        (block): Sequential(
          (0): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (1): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(960, 960, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=960, bias=False)
              (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Hardswish()
          )
          (2): QuantizableSqueezeExcitation(
            (fc1): ConvReLU2d(
              (0): Conv2d(960, 240, kernel_size=(1, 1), stride=(1, 1))
              (1): ReLU()
            )
            (relu): Identity()
            (fc2): Conv2d(240, 960, kernel_size=(1, 1), stride=(1, 1))
            (skip_mul): FloatFunctional(
              (activation_post_process): Identity()
            )
          )
          (3): ConvBNActivation(
            (0): ConvBn2d(
              (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(160, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
            )
            (1): Identity()
            (2): Identity()
          )
        )
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (16): ConvBNActivation(
        (0): ConvBn2d(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        )
        (1): Identity()
        (2): Hardswish()
      )
    )
    (fpn): FeaturePyramidNetwork(
      (inner_blocks): ModuleList(
        (0): Conv2d(160, 256, kernel_size=(1, 1), stride=(1, 1))
        (1): Conv2d(960, 256, kernel_size=(1, 1), stride=(1, 1))
      )
      (layer_blocks): ModuleList(
        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
      (extra_blocks): LastLevelMaxPool()
    )
  )
  (rpn): RegionProposalNetwork(
    (anchor_generator): AnchorGenerator()
    (head): RPNHead(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (cls_logits): Conv2d(256, 15, kernel_size=(1, 1), stride=(1, 1))
      (bbox_pred): Conv2d(256, 60, kernel_size=(1, 1), stride=(1, 1))
    )
  )
  (roi_heads): RoIHeads(
    (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)
    (box_head): TwoMLPHead(
      (fc6): Linear(in_features=12544, out_features=1024, bias=True)
      (fc7): Linear(in_features=1024, out_features=1024, bias=True)
    )
    (box_predictor): FastRCNNPredictor(
      (cls_score): Linear(in_features=1024, out_features=2, bias=True)
      (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)
    )
  )
)