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Describe the bug box_iou() does not work as expected when boxes1dtype is int, resulting in returned IoU=0 (after converting back to intdtype). No warning messages are generated in this case.
Expected behavior
All IoUs should be (in this case) values close to one. However, if boxes1.dtype is of type int
the line in the definition of box_iou(): iou_t = iou_t.to(dtype=box_dtype)
makes those IoUs to be 0. Maybe casting the IoU result back to integer should be avoided or at least warning message can be generated (iou_t is always in range [0.; 1.] just before type conversion).
================================
Printing system config...
System: Linux
Linux version: Ubuntu 22.04.5 LTS
Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.35
Processor: x86_64
Machine: x86_64
Python version: 3.9.20
Process name: python
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Num physical CPUs: 16
Num logical CPUs: 32
Num usable CPUs: 32
CPU freq. (MHz): 4467
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 62.0
Available memory (GB): 40.0
Used memory (GB): 21.3
Describe the bug
box_iou()
does not work as expected whenboxes1
dtype
isint
, resulting in returned IoU=0
(after converting back toint
dtype
). No warning messages are generated in this case.To Reproduce
Steps to reproduce the behavior:
monai
.OUTPUT:
Expected behavior
All IoUs should be (in this case) values close to one. However, if
boxes1.dtype
is of typeint
the line in the definition of
box_iou()
:iou_t = iou_t.to(dtype=box_dtype)
makes those IoUs to be 0. Maybe casting the IoU result back to integer should be avoided or at least warning message can be generated (
iou_t
is always in range[0.; 1.]
just before type conversion).Environment
================================
Printing MONAI config...
MONAI version: 1.4.0
Numpy version: 1.26.4
Pytorch version: 2.5.0
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 46a5272
MONAI file: /home//anaconda3/envs/crc/lib/python3.9/site-packages/monai/init.py
Optional dependencies:
Pytorch Ignite version: 0.4.11
ITK version: 5.4.2
Nibabel version: 5.3.2
scikit-image version: 0.22.0
scipy version: 1.13.1
Pillow version: 10.4.0
Tensorboard version: 2.19.0
gdown version: 5.2.0
TorchVision version: 0.20.0
tqdm version: 4.66.5
lmdb version: 1.6.2
psutil version: 6.0.0
pandas version: 2.2.2
einops version: 0.8.1
transformers version: 4.40.2
mlflow version: 2.20.2
pynrrd version: 1.1.3
clearml version: 1.17.2rc0
For details about installing the optional dependencies, please visit:
https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies
================================
Printing system config...
System: Linux
Linux version: Ubuntu 22.04.5 LTS
Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.35
Processor: x86_64
Machine: x86_64
Python version: 3.9.20
Process name: python
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Num physical CPUs: 16
Num logical CPUs: 32
Num usable CPUs: 32
CPU freq. (MHz): 4467
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 62.0
Available memory (GB): 40.0
Used memory (GB): 21.3
================================
Printing GPU config...
Num GPUs: 2
Has CUDA: True
CUDA version: 11.8
cuDNN enabled: True
NVIDIA_TF32_OVERRIDE: None
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None
cuDNN version: 90100
Current device: 0
Library compiled for CUDA architectures: ['sm_50', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'sm_37', 'sm_90', 'compute_37']
GPU 0 Name: NVIDIA GeForce RTX 4090
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 128
GPU 0 Total memory (GB): 23.6
GPU 0 CUDA capability (maj.min): 8.9
GPU 1 Name: NVIDIA GeForce RTX 4090
GPU 1 Is integrated: False
GPU 1 Is multi GPU board: False
GPU 1 Multi processor count: 128
GPU 1 Total memory (GB): 23.6
GPU 1 CUDA capability (maj.min): 8.9
Additional context
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