Panoptic Quality Metrics (PQ): Unexpected PQ value during model evaluation.

I'm using a custom dataset (fine-tuning) for the panoptic task using R101-FPN as COCO Panoptic Segmentation baseline (Panoptic FPN).

Both training and inferencing are able to obtain good result. However, when I perform model evaluation I got the following result:

Panoptic Evaluation Results:
| | PQ | SQ | RQ | #categories |
|:------:|:-------:|:-------:|:------:|:-------------:|
| All | 669.439 | 785.069 | 68.472 | 71 |
| Things | 51.593 | 75.233 | 66.680 | 2 |
| Stuff | 687.348 | 805.644 | 68.524 | 69 |

Evaluation metrics:
{
'PQ': np.float64(669.4392318591345),
'SQ': np.float64(785.0691309166493),
'RQ': 68.47162219667933,
'PQ_th': np.float64(51.592946379865104),
'SQ_th': np.float64(75.23326285684394),
'RQ_th': 66.68010678527925,
'PQ_st': np.float64(687.3478198440408),
'SQ_st': np.float64(805.6440836140348),
'RQ_st': 68.52355017961847
}

I'm not sure why SQ_st is in the hundreds range which I believe it causes PQ to be in hundreds range too. Would appreciate help if anyone could point out any possible issues. Thanks.

## Instructions To Reproduce the Issue:
Unable to provide data to reproduce the issue.

## Source code:

self._cfg.merge_from_file(self._trained_config_path)
self._predictor = DefaultPredictor(self._cfg)
self._initialize_datasets()

evaluator = COCOPanopticEvaluator(
dataset_name=eval_dataset_name,
output_dir=self._output_dir,
)
val_loader = build_detection_test_loader(self._cfg, eval_dataset_name)

results = inference_on_dataset(self._predictor.model, val_loader, evaluator)

## Environment:
------------------------------- ---------------------------------------------------------------------------------------------------------------------------
sys.platform linux
Python 3.12.7 (main, Oct 16 2024, 04:37:19) [Clang 18.1.8 ]
numpy 1.26.4
detectron2 0.6 @/pvc-data/workspaces/xxx/xxx/.venv/lib/python3.12/site-packages/detectron2
Compiler clang 18.1.3
CUDA compiler not available
DETECTRON2_ENV_MODULE
PyTorch 2.5.1+cu124 @/pvc-data/workspaces/xxx/xxx/.venv/lib/python3.12/site-packages/torch
PyTorch debug build False
torch._C._GLIBCXX_USE_CXX11_ABI False
GPU available No: torch.cuda.is_available() == False
Pillow 11.0.0
torchvision 0.20.1+cu124 @/pvc-data/workspaces/xxx/xxx/.venv/lib/python3.12/site-packages/torchvision
fvcore 0.1.5.post20221221
iopath 0.1.9
cv2 4.10.0
------------------------------- ---------------------------------------------------------------------------------------------------------------------------
PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.5.1, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

Start with COCOPanopticEvaluator and the inference_on_dataset call, then trace the inputs produced by build_detection_test_loader and DefaultPredictor. Check how the custom dataset's panoptic annotations and evaluation outputs are represented, and compare the computed PQ, SQ, and RQ values with the expected metric ranges. No reproduction data or specific source file is provided, so completion cannot be confirmed from the issue alone.

Written by the indexing model from the issue text.