Faster R-CNN Add new Classifier (how to?)

Hello guys, currently i have task to make object detection. Here I am trying to use Faster R-CNN, which is already pretrained, and later I will do fine tuning.

I also have another task, a (special) classification based on the input entered into the Faster R-CNN model, where my plan is to take the results of feature extraction from the “backbone”.

sorry in advance if it's wrong, but i tried it like this.

First, code to create a Faster R-CNN with a custom class.

def get_fasterrcnn(num_classes, pretrained=True):
    # load a model pre-trained on COCO
    if pretrained:
        w = torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT
        model = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(weights=w)
        model = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(weights=None)

    # get number of input features for the classifier
    in_features = model.roi_heads.box_predictor.cls_score.in_features

    # replace the pre-trained head with a new one
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

    return model

then next, I tried to take a feature map from the “backbone” layer (256? don’t know for sure) and I used it for my task custom classification for check fuel system.

# Create Custom Model
class CustomModelFuelSystem(nn.Module):
    def __init__(self, in_channels, out_channel=1):

        self.fuel_system_layers = nn.Sequential(
            nn.Linear(256, 128),
            nn.Linear(128, out_channel)

    def forward(self, x):
        return self.fuel_system_layers(x)

after that I combined the Faster R-CNN model and the Custom model, like this
class CustomFRCNN(nn.Module):
    def __init__(self, model, custom_model):

        self.faster_rcnn = model
        self.fuel_system = custom_model

    def forward(self, x):

        # do forward pass for model Faster R-CNN
        f_rcnn = self.faster_rcnn(x)

        # do forward pass for custom model layers
        ##  Get the output of the backbone and detach it
        backbone_output = f_rcnn[0]['backbone']

        fuel_sys = self.fuel_system(backbone_output)

        return f_rcnn, fuel_sys

Next, I created these models for initialization
# Create Model Faster R-CNN
model_rcnn = get_fasterrcnn(num_classes, pretrained=True)

# Create Model Custom Task
in_features_fuel = model_rcnn.backbone.out_channels
model_fuel = CustomModelFuelSystem(in_channels=in_features_fuel, out_channel=1)

# Create Model Custom FRCNN Task
model = CustomFRCNN(model=model_rcnn, custom_model=model_fuel)

but when we try to classify with dummy data, there is an error like this:
# Trying forward pass for dummies

# check sanity model
img_size = 800
x = torch.randn((1, 3, img_size, img_size))


test = model(x)

print(f"Input shape:\n {x.shape} \n")

I’m also confused, maybe someone can help?