How to save Detectron model as Vanilla Pytorch model?

I have a `Faster-RCNN` model trained with `Detectron2`. Model weights are saved as `model.pth`.

I have my `config.yml` file and there are a couple of ways to load this model:

```
from detectron2.modeling import build_model
from detectron2.checkpoint import DetectionCheckpointer

cfg = get_cfg()
config_name = "config.yml"
cfg.merge_from_file(config_name)

cfg.MODEL.WEIGHTS = './model.pth'
model = DefaultPredictor(cfg)

OR

model_ = build_model(cfg)
model = DetectionCheckpointer(model_).load("./model.pth")
```

Also, you can get predictions from this model individually as [given in official documentation](https://detectron2.readthedocs.io/en/latest/tutorials/models.html#model-input-format):
```
image = np.array(Image.open('page4.jpg'))[:,:,::-1] # RGB to BGR format
tensor_image = torch.from_numpy(image.copy()).permute(2, 0, 1) # B, channels, W, H

with torch.no_grad():
output = torch_model([{"image":tensor_image}])
```

running the following commands:
```
print(type(model))
print(type(model.model))
print(type(model.model.backbone))
```

Gives you:
```

```

**Problem: I want to use [GradCam for model explainability](https://github.com/jacobgil/pytorch-grad-cam) and it uses `pytorch` models as [given in this tutorial](https://analyticsindiamag.com/explainable-image-classification-using-faster-r-cnn-and-grad-cam/)**

How can I turn `detectron2` model in vanilla `pytorch` model?

I have tried:
```
torch.save(model.model.state_dict(), "torch_weights.pth")
torch.save(model.model, "torch_model.pth")

from torchvision.models.detection import fasterrcnn_resnet50_fpn

dummy = fasterrcnn_resnet50_fpn(pretrained=False, num_classes=1)
# dummy.load_state_dict(torch.load('./model.pth', map_location = 'cpu'))
dummy.load_state_dict(torch.load('./torch_weights.pth', map_location = 'cpu'))
```

but obviously, I'm getting errors due to the different layer names and sizes etc.