Getting segmentation fault when running InferenceSlicer with OBB model with thread_workers > 1

[zxsk1974]

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• [X] I have searched the Supervision issues and found no similar bug report.

Bug

InferenceSlicer throws Segmentation fault with thread_workers = 4:

Segmentation fault (core dumped)

Environment

Supervision 0.24.0 Python 3.11

Minimal Reproducible Example

from ultralytics import YOLO
import cv2
import sys
import torch
import numpy as np
from pathlib import Path
import math
import supervision as sv

def callback(image_slice: np.ndarray) -> sv.Detections:
    result = model(image_slice, conf=0.6)
    print("Results:", result)
    return sv.Detections.from_ultralytics(result[0])

model = YOLO('yolo11x-obb.pt')

print("GPU:", torch.cuda.is_available())
if torch.cuda.is_available():
    device = torch.device("cuda")
    print("Using GPU:", torch.cuda.get_device_name())

# load image
file_name = Path(sys.argv[1])
image = cv2.imread(sys.argv[1]) #Image.open(file_name, mode='r')
print("Loaded image:", file_name)

image_wh = (image.shape[1], image.shape[0])
slice_wh = (1024, 1024)
overlap_ratio_wh = (0.2, 0.2)
overlap_ratio_w, overlap_ratio_h = overlap_ratio_wh
slice_w, slice_h = slice_wh
overlap_wh = (math.ceil(slice_w * overlap_ratio_w), math.ceil(slice_h * overlap_ratio_h))

slicer = sv.InferenceSlicer(
  callback=callback,
  overlap_filter=sv.OverlapFilter.NON_MAX_MERGE,
  iou_threshold=0.15,
  slice_wh=slice_wh,
  overlap_ratio_wh=None,
  overlap_wh=overlap_wh,
  thread_workers=2
)

detections = slicer(image)

labels = [
    f"{class_name} {confidence:.1f}"
    for class_name, confidence
    in zip(detections['class_name'], detections.confidence)
]

label_annotator = sv.LabelAnnotator(text_scale=0.2, text_thickness=1, text_padding=0, text_position=sv.Position.TOP_LEFT)
bbox_annotator = sv.BoxAnnotator(color=sv.ColorPalette.DEFAULT.colors[6], thickness=2)
obb_annotator = sv.OrientedBoxAnnotator(color=sv.ColorPalette.DEFAULT.colors[6], thickness=2)

print(f"Image shape: {image_wh[0]}w x {image_wh[1]}h")
print(f"Tile size: {slice_wh[0]}w x {slice_wh[1]}h")
print(f"Overlap: {overlap_wh[0]}w x {overlap_wh[1]}h. Ratio {overlap_ratio_wh}")
print(f"Overlap Filter: {sv.OverlapFilter.NON_MAX_MERGE}")
print(f"Found {len(detections)} objects")

annotated_image = obb_annotator.annotate(scene=image.copy(), detections=detections)
annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)

cv2.imwrite(file_name.stem + "-output.jpg", annotated_image)
![airplane-graveyard-zoomed](https://github.com/user-attachments/assets/0bf79b23-4775-43f3-8573-bfe0e96cbf91)

Additional

To reproduce:

python detect-image-slicer-bug.py airplane-graveyard-zoomed.jpg

.jpg

Are you willing to submit a PR?

• [ ] Yes I'd like to help by submitting a PR!

[LinasKo]

Hi @zxsk1974 👋

Thank you for reporting it. Unfortunately, many models aren't made to be run from multiple threads. We plan to shift to running in batches (linked PR), but it is unfortunately relatively low on the priority list.

[zxsk1974]

@LinasKo batching will work too, can I try it on pre-release branch?

[LinasKo]

Not as a pre-release branch. I don't expect to merge this any time soon.

However, I brought the branch up-to-date with the latest supervision version. Feel free to install via pip install git+https://github.com/LinasKo/supervision.git@feature/batched-inference-slicer.

Alternatively, you may fork it from my repo. Install it in the same way, but from your own namespace.

[LinasKo]

Let us know if it works - that'd give more reason to revisit the PR 🙂

[zxsk1974]

Interesting, but sometimes the Slicer with 2 threads works till the end of detection and than throws the error, but not segmentation fault. This is still released version of SV. The error:

Traceback (most recent call last): File "/home/sergey/Workspace/detection-tiling-custom-obb/detect-image-slicer.py", line 89, in detections = slicer(image) ^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/supervision/detection/tools/inference_slicer.py", line 162, in call detections_list.append(future.result()) ^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/concurrent/futures/_base.py", line 449, in result return self.get_result() ^^^^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/concurrent/futures/_base.py", line 401, in get_result raise self._exception File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/concurrent/futures/thread.py", line 58, in run result = self.fn(self.args, self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/supervision/detection/tools/inference_slicer.py", line 191, in _run_callback detections = self.callback(image_slice) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/sergey/Workspace/detection-tiling-custom-obb/detect-image-slicer.py", line 50, in callback result = model(image_slice, conf=0.6) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/ultralytics/engine/model.py", line 176, in call return self.predict(source, stream, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/ultralytics/engine/model.py", line 547, in predict self.predictor.setup_model(model=self.model, verbose=is_cli) File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/ultralytics/engine/predictor.py", line 303, in setup_model self.model = AutoBackend( ^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/ultralytics/nn/autobackend.py", line 145, in init model = model.fuse(verbose=verbose) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/ultralytics/nn/tasks.py", line 208, in fuse delattr(m, "bn") # remove batchnorm ^^^^^^^^^^^^^^^^ File "/home/sergey/anaconda3/envs/yolo/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1799, in delattr super().delattr(name) AttributeError: 'Conv' object has no attribute 'bn'

[LinasKo]

Could be an ultralytics version issue. Unless you need yolo11, I'd try ultralytics==8.2.103.

[LinasKo]

Hi @zxsk1974 👋

Would you mind testing if the autodistill changes made a difference?

pip install git+https://github.com/autodistill/autodistill.git@feat/supervision-0.24.0-support