Input: - memory: bs, \sum{hw}, d_model - memory_padding_mask: bs, \sum{hw} - spatial_shapes: nlevel, 2 - learnedw

Generalized IoU from https://giou.stanford.edu/ The boxes should be in [x0, y0, x1, y1] format Returns a [N, M] pairwise matrix, where

Generalized IoU from https://giou.stanford.edu/ Input: - boxes1, boxes2: N,4 Output: - giou: N, 4

L1-normalize columns of X

Compute the bounding boxes around the provided masks The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatia

Compute the bounding boxes around the provided masks The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spati

This property is a generalization of size_divisibility. Some backbones and training recipes require specific padding constraints, suc

Some backbones require the input height and width to be divisible by a specific integer. This is typically true for encoder / decoder