Why are the feature dimensions different?

[chenlin038]

Questions like:
I use the following code to extract features based on the FPN model. The config is:
"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"

Feature extraction code:
'''
img_path = "input.jpg"
img_ori = cv2.imread(img_path)
height, width = img_ori.shape[:2]
img = predictor.transform_gen.get_transform(img_ori).apply_image(img_ori)
img = torch.as_tensor(img.astype("float32").transpose(2, 0, 1))
inputs = [{"image": img, "height": height, "width": width}]
with torch.no_grad():
imglists = predictor.model.preprocess_image(inputs) # don't forget to preprocess
features = predictor.model.backbone(imglists.tensor) # set of cnn features
proposals, _ = predictor.model.proposal_generator(imglists, features, None) # RPN

proposal_boxes = [x.proposal_boxes for x in proposals]
features_list = [features[f] for f in predictor.model.roi_heads.in_features]
proposal_rois = predictor.model.roi_heads.box_pooler(features_list,  proposal_boxes)
**box_features** = predictor.model.roi_heads.box_head(proposal_rois) 

'''

I use box_features as the feature of the object detection. But its dimension is 1024, which is inconsistent with the original bottom-up-attention image feature dimension in 2048. They all use residual-101 as the backbone network, so why are the feature dimensions inconsistent?

I apologized if the answer is obvious, I am very new to object detection.

Thank you!

[airsplay]

The Github version is converted from the original bottom-up attention repo. It takes a specific detector trained with Visual Genome. Please take the configurations under the folder py-bottom-up-attention/configs/VG-Detection/. Hope these help!