RandHistogramShift transform gives nan values for input when all values are 0 #5875
Description
Describe the bug
RandHistogramShift transform gives nan values when an input with all zeros is passed to it.
To Reproduce
import monai
import torch
transform = monai.transforms.RandHistogramShift(prob=1)
tensor = torch.zeros((2, 2))
print(transform(tensor))
gives the following result,
tensor([[nan, nan],
[nan, nan]])
Expected behavior
The returned tensor should contain zeros instead of nan values.
In a previous version of monai, this worked fine as it was called on a ndarray by default. In the current version, even if an ndarray is passed, the same nan behaviour is thrown. It looks like the functionality to handle a ndarray
MONAI/monai/transforms/intensity/array.py
Line 1433 in bdf5e1e
is never called because of
MONAI/monai/transforms/intensity/array.py
Line 1461 in bdf5e1e
Environment
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Printing MONAI config...
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MONAI version: 1.1.0+29.gbdf5e1ec
Numpy version: 1.23.5
Pytorch version: 1.13.1+cu117
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: bdf5e1ec15724ecf4e3634955cf34a35843a3e3e
MONAI __file__: /home/suraj/anaconda3/envs/monai-dev/lib/python3.8/site-packages/monai/__init__.py
Optional dependencies:
Pytorch Ignite version: 0.4.10
ITK version: 5.3.0
Nibabel version: 5.0.0
scikit-image version: 0.19.3
Pillow version: 9.4.0
Tensorboard version: 2.11.2
gdown version: 4.6.0
TorchVision version: 0.14.1+cu117
tqdm version: 4.64.1
lmdb version: 1.4.0
psutil version: 5.9.4
pandas version: 1.5.2
einops version: 0.6.0
transformers version: 4.21.3
mlflow version: 2.1.1
pynrrd version: 1.0.0
For details about installing the optional dependencies, please visit:
https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies
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Printing system config...
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System: Linux
Linux version: Ubuntu 20.04.4 LTS
Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.10
Processor: x86_64
Machine: x86_64
Python version: 3.8.15
Process name: python
Command: ['python']
Num physical CPUs: 12
Num logical CPUs: 12
Num usable CPUs: 12
CPU usage (%): [13.4, 8.7, 11.2, 10.7, 8.4, 6.8, 5.8, 5.7, 50.0, 8.4, 6.0, 27.5]
CPU freq. (MHz): 914
Load avg. in last 1, 5, 15 mins (%): [6.7, 13.4, 47.2]
Disk usage (%): 78.3
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 250.6
Available memory (GB): 93.0
Used memory (GB): 5.7
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Printing GPU config...
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Num GPUs: 4
Has CUDA: True
CUDA version: 11.7
cuDNN enabled: True
cuDNN version: 8500
Current device: 0
Library compiled for CUDA architectures: ['sm_37', 'sm_50', 'sm_60', 'sm_70', 'sm_75', 'sm_80', 'sm_86']
GPU 0 Name: Quadro RTX 8000
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 72
GPU 0 Total memory (GB): 47.5
GPU 0 CUDA capability (maj.min): 7.5
GPU 1 Name: Quadro RTX 8000
GPU 1 Is integrated: False
GPU 1 Is multi GPU board: False
GPU 1 Multi processor count: 72
GPU 1 Total memory (GB): 47.5
GPU 1 CUDA capability (maj.min): 7.5
GPU 2 Name: Quadro RTX 8000
GPU 2 Is integrated: False
GPU 2 Is multi GPU board: False
GPU 2 Multi processor count: 72
GPU 2 Total memory (GB): 47.5
GPU 2 CUDA capability (maj.min): 7.5
GPU 3 Name: Quadro RTX 8000
GPU 3 Is integrated: False
GPU 3 Is multi GPU board: False
GPU 3 Multi processor count: 72
GPU 3 Total memory (GB): 47.5
GPU 3 CUDA capability (maj.min): 7.5