Howdy,
Applying RoIAlign (torchvision.ops.RoIAlign) on rois that is a tensor of shape (K, 5) results in a segfault : Process finished with exit code 139 (interrupted by signal 11: SIGSEGV).
The faulty behavior is not observed when :

Applying RoIAlign on rois that is a list of tensor of shape (4,)

RoIAlign input size is small enough (typically LEQ 32,10,10)

Here is a code snippet that should allow you to reproduce my observations.
import argparse

import torch
from torchvision.ops import RoIAlign

NB_ROI_PER_DOC = 5

if __name__ == "__main__":

    torch.manual_seed(0)

    parser = argparse.ArgumentParser(description='minimum reproducible example')
    parser.add_argument('--bug', action='store_true', default=False, help='toggle on the issue')
    parser.add_argument('--batch-size', type=int, default=1)
    parser.add_argument('--input-size', type=str, default="64,10,10")
    args = parser.parse_args()

    align = RoIAlign((3, 3), spatial_scale=14 / 224, sampling_ratio=2)
    batch_size = args.batch_size
    input_ = torch.randn((batch_size, *(int(dim) for dim in args.input_size.split(","))))
    rois = [torch.abs(torch.randn(NB_ROI_PER_DOC, 4)) for _ in input_]

    if args.bug:
        nb_rois = NB_ROI_PER_DOC * batch_size
        boxes = torch.abs(torch.randn(nb_rois, 4))
        roi_ids = torch.arange(nb_rois).view(-1, 1)
        rois = torch.cat((roi_ids, boxes), dim=1)

    output = align(input=input_, rois=rois)
    print(rois)
    print(output.shape)

Running the code in docker to be freeze packages versions and the like.
FROM pytorch/pytorch:1.10.0-cuda11.3-cudnn8-runtime
RUN mkdir -p /opt/debug
WORKDIR /opt/debug
ADD mre.py .
ENTRYPOINT ["python", "/opt/debug/mre.py"]


Sample outputs :
Problematic behavior, to run : build -f Dockerfile.minimal -t debug-torch:v0 . ; docker run  docker run debug-torch:v0 --bug; docker ps -a |grep debug-torch:v0  results with:
46b40fe6efee   debug-torch:v0 "python /opt/debug/m…"     16 seconds ago   Exited (139) 

Expected behavior, to run : docker run  docker run debug-torch:v0 results with:
[tensor([[1.4164, 0.2379, 0.9334, 1.1331],
        [0.3530, 2.0928, 0.6356, 1.5069],
        [0.9527, 1.0599, 0.9549, 1.3355],
        [0.5251, 0.7416, 0.4269, 0.4008],
        [0.7872, 0.0834, 1.1256, 1.5490]])]
torch.Size([5, 64, 3, 3])

To reduce the input size solves the problem, to run docker run  docker run debug-torch:v0 --bug --input-size 5,5,5 results with:
tensor([[0.0000, 0.3584, 1.5616, 0.3546, 1.0811],
        [1.0000, 0.8760, 0.2871, 1.0216, 0.5111],
        [2.0000, 1.7137, 0.5101, 0.4749, 0.6334],
        [3.0000, 1.2063, 0.6074, 0.5472, 1.1005],
        [4.0000, 0.7201, 0.0119, 0.3398, 0.2635]])
torch.Size([5, 64, 3, 3])


I’m well aware that the most likely reason for the problem is me missing out something obvious in the using of RoIAlign, feel free to simply let me know if this is the case.
Thank for reading down to this point & have a nice day