Error using sequential on faster-rcnn to get regional proposal network object features

I’m trying to use pretrained FasterRCNN network provided in torchvision. model=torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) model.eval() model([image_tensor]) working perfectly fine but when I use sequential to stop at intermediate layers like this model2=nn.Sequential(*list(model.children())[:-2]) to use until RPN network I’m getting error when passing a image as show below TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not tuple

So then, I tried to inherit nn.module and implement based on Generalized RCNN and faster-rcnn.py as shown here but instead of Kx2048 features I’m getting 862x4. feature vector.

[ananthpn]

Thanks much @fmassa your solution works so well for me. I used this in my application and am posting here your code wrapped as a function for any one who may have a similar requirement:

[fmassa]

No worries! Let us know if you have any other comment / feedback