Lets assume a torch.Tensor bboxes of size [# bboxes, 4] where the last dimension represents the dimensions of a single bounding box in the order left, top, bottom, right.
Lets also assume there is a torch.Tensor points of size [# points, 2] where the last dimension represents the dimensions of a single point in the order x, y.
All coordinates are in a system where the origin is located at the top-left corner, and the axis spans right-down.
import torch
# Bounding boxes and points.
bboxes = [[0, 0, 30, 30], [50, 50, 100, 100]]
bboxes = torch.FloatTensor(bboxes)
points = [[99, 50], [101, 0], [30, 30]]
points = torch.FloatTensor(points)
# Keep a reference to the original strided `points` tensor.
old_points = points
# Permutate all points for every single bounding box.
points = points.unsqueeze(1)
points = points.repeat(1, len(bboxes), 1)
# Create the conditions necessary to determine if a point is within a bounding box.
# x >= left, x <= right, y >= top, y <= bottom
c1 = points[:, :, 0] <= bboxes[:, 2]
c2 = points[:, :, 0] >= bboxes[:, 0]
c3 = points[:, :, 1] <= bboxes[:, 3]
c4 = points[:, :, 1] >= bboxes[:, 1]
# Add all of the conditions together. If all conditions are met, sum is 4.
# Afterwards, get all point indices that meet the condition (a.k.a. all non-zero mask-summed values)
mask = c1 + c2 + c3 + c4
mask = torch.nonzero((mask == 4).sum(dim=-1)).squeeze()
# Select all points that meet the condition.
print(old_points.index_select(dim=0, index=mask))
Enjoy! :).