Faster implementation of torchvision.ops.boxes::masks_to_boxes

[atharvas]

Quoting #8184

🚀 The feature

torchvision.ops.boxes::masks_to_boxes is used to convert a batch of binary 2D image masks to a set of bounding boxes. Essentially, for a mask of shape $(B, 64, 64)$, masks_to_boxes allocates a tensor of shape $(B, 4)$ and calculates the bounding box for each element of the batch sequentially. This is $O(B)$ storage and $O(B)$ runtime and the simplest implementation possible.

This proposal pertains to creating a faster and more general version of this function.

Profiling

Some primitive performance benchmarking validates this hypothesis. Profiling code is here. Profiling was done on an Apple M2 Pro.

Memory Profiling

Speed Profiling

Correctness

The behavior of the function is unchanged. There is a single test case for testing masks_to_boxes (test.test_ops:test_masks_box). The new implementation passes this test case.

$ pytest test/test_ops.py -vvv -k test_masks_box
============================================= test session starts =============================================
platform darwin -- Python 3.12.0, pytest-7.4.4, pluggy-1.3.0 -- /Users/atharvas/miniconda3/envs/torchvision/bin/python
cachedir: .pytest_cache
rootdir: /Users/atharvas/Desktop/projects/000_uncategorized/vision
configfile: pytest.ini
plugins: mock-3.12.0
collected 1440 items / 1439 deselected / 1 selected                                                           

test/test_ops.py::TestMasksToBoxes::test_masks_box PASSED                                               [100%]

============================================== warnings summary ===============================================
../../../../miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1209
  /Users/atharvas/miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1209: DeprecationWarning: ast.Str is deprecated and will be removed in Python 3.14; use ast.Constant instead
    elif isinstance(value, ast.Str):

../../../../miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1210
  /Users/atharvas/miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1210: DeprecationWarning: Attribute s is deprecated and will be removed in Python 3.14; use value instead
    s += value.s

test/test_ops.py::TestMasksToBoxes::test_masks_box
  /Users/atharvas/miniconda3/envs/torchvision/lib/python3.12/site-packages/_pytest/python.py:194: ResourceWarning: unclosed file <_io.BufferedReader name='/Users/atharvas/Desktop/projects/000_uncategorized/vision/test/assets/masks.tiff'>
    result = testfunction(**testargs)
  Enable tracemalloc to get traceback where the object was allocated.
  See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=============================== 1 passed, 1439 deselected, 3 warnings in 0.86s ================================

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/vision/8194

• 📄 Preview Python docs built from this PR

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[NicolasHug]

Thanks for the PR @atharvas

The test failure in https://github.com/pytorch/vision/actions/runs/7394640746/job/20496609120?pr=8194 seems relevant, as SimpleCopyPaste internally relies on masks_to_boxes.

But beyond the correctness issue, I am wondering whether such a change would bring a critical improvement. Saving 1MB isn't really a problem, and the time performance benefits are unclear when batch size is reasonable (i.e. < 1024?). Considering the proposed code is a lot more complex than the previous one, we should only be comfortable merging this PR if it could remove a known bottleneck. Did you find that masks_to_boxes performance was problematic in your own training runs?

[atharvas]

Hi! Thanks for the update. Just fixed the error after recreating it on a linux box. Sorry about that!

(torchvision) asehgal@ubuntu:~/vision$ pytest test/test_ops.py -vvv -k test_masks_box
================================================== test session starts ==================================================
platform linux -- Python 3.12.0, pytest-7.4.0, pluggy-1.0.0 -- /home/asehgal/env/miniconda3/envs/torchvision/bin/python
cachedir: .pytest_cache
rootdir: /home/asehgal/vision
configfile: pytest.ini
plugins: mock-3.10.0
collected 1440 items / 1439 deselected / 1 selected                                                                     

test/test_ops.py::TestMasksToBoxes::test_masks_box PASSED                                                         [100%]

=================================================== warnings summary ====================================================
../env/miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1209
  /home/asehgal/env/miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1209: DeprecationWarning: ast.Str is deprecated and will be removed in Python 3.14; use ast.Constant instead
    elif isinstance(value, ast.Str):

../env/miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1210
  /home/asehgal/env/miniconda3/envs/torchvision/lib/python3.12/site-packages/torch/jit/frontend.py:1210: DeprecationWarning: Attribute s is deprecated and will be removed in Python 3.14; use value instead
    s += value.s

test/test_ops.py::TestMasksToBoxes::test_masks_box
  /home/asehgal/env/miniconda3/envs/torchvision/lib/python3.12/site-packages/_pytest/python.py:194: ResourceWarning: unclosed file <_io.BufferedReader name='/home/asehgal/vision/test/assets/masks.tiff'>
    result = testfunction(**testargs)
  Enable tracemalloc to get traceback where the object was allocated.
  See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info.

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================== 1 passed, 1439 deselected, 3 warnings in 1.76s =====================================
(torchvision) asehgal@ubuntu:~/vision$ pytest -s test/test_prototype_transforms.py
================================================== test session starts ==================================================
platform linux -- Python 3.12.0, pytest-7.4.0, pluggy-1.0.0
rootdir: /home/asehgal/vision
configfile: pytest.ini
plugins: mock-3.10.0
collected 24 items                                                                                                      

test/test_prototype_transforms.py ........................

================================================== 24 passed in 2.32s ===================================================
(torchvision) asehgal@ubuntu:~/vision$ 

About whether this is a critical improvement

For my use case yes, however, I'm not sure for the community at large. My codebase is private right now, but here's an example of this bottleneck "in the wild:"

This function processes a batch of B videos, each of trajectory length T, and containing N slots (slots=object masks, for what it's worth). Notice that the batch size is changed to B*T*N

Inside this function, vops.masks_to_boxes is called with the expectation it returns fast.

However, because the apparent batch size is actually B*T*N (In validation this is 64*64*7=28672 and in training it's 64*10*7=4480 per GPU) the operation ends up becoming a subtle bottleneck. Now, in my codebase, I need to call masks_to_boxes in every iteration of the training loop, which further increases the runtime.

[NicolasHug]

Thank you for the details @atharvas . That SGTM. Before merging, do you mind running the benchmark on CUDA as well to make sure there's no slow-down for GPUs? If that were to be the case, we could just have 2 paths (one for CPU, one for GPU)

[atharvas]

Hi!
Here are the results running on a GPU [updated script here]. This is an NVIDIA RTX 2080 Ti. I increased the max batch size to $2^{15}$.

Speed Comparison:

Peak Memory Comparison:

The second graph is interesting! It looks like the new implementation uses up more VRAM (in line with @pmeier's commend in #8184 ). I did an analysis of the GPU allocations using torch.cuda.memory_snapshot and each intermediate call to torch.any and torch.argmax allocates a $(B, 64)$ and $(B, 1)$ sized tensor on the GPU. The memory is reclaimed as soon as the function exits. Hence, the peak memory is higher for the new implementation, while the "active allocated" memory is the same as the original function.

@NicolasHug Do you think this warrants a separate pathway for CPU and GPU?