How to extract feature from any layer in a pre-trained detector?

Thanks for your great work! The performances of the detectors are amazing!

I have some questions on detectron2.

1. Now I have a list of bbox coordinates with respect to some images, how can I run a pre-trained detector (i.e. Faster-RCNN-R50-FPN) on these images, and crop the feature map (i.e. extract the 7x7 rois in the detector) according to the bbox coordinates? Is there an API in detectron2 to do the work?
2. How can I generate the proposals for Fast-RCNN, should I run the RPN directly to get the .pkl file?

Thank you again for your attention!

[Programmnix]

Hi,

for everybody how is interested in feature extraction for bounding boxes. There is a fork of this repo with example code. https://github.com/airsplay/py-bottom-up-attention

[ppwwyyxx]

UPDATE: see https://detectron2.readthedocs.io/tutorials/models.html#partially-execute-a-model

from detectron2.modeling import build_backbone
from detectron2.modeling.poolers import ROIPooler
backbone = build_backbone(cfg)
pooler = ROIPooler(...)
rois = pooler(backbone(NCHW_images), [kx4 tensors])

1. Running inference on a dataset with a RPN-only model will produce the proposals for the dataset in output/inference. An example command is

python tools/train_net.py --config-file configs/COCO-Detection/rpn_R_50_FPN_1x.yaml --eval-only