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Printing MONAI config...
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MONAI version: 1.4.0
Numpy version: 1.26.4
Pytorch version: 1.12.1
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008
MONAI __file__: /home/<username>/anaconda3/envs/workspace/lib/python3.10/site-packages/monai/__init__.py
Optional dependencies:
Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.
ITK version: 5.4.0
Nibabel version: 5.1.0
scikit-image version: 0.22.0
scipy version: 1.11.1
Pillow version: 9.4.0
Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.
gdown version: NOT INSTALLED or UNKNOWN VERSION.
TorchVision version: 0.13.1
tqdm version: 4.65.0
lmdb version: NOT INSTALLED or UNKNOWN VERSION.
psutil version: 6.0.0
pandas version: 2.2.3
einops version: 0.8.0
transformers version: 4.24.0
mlflow version: 2.14.0
pynrrd version: NOT INSTALLED or UNKNOWN VERSION.
clearml version: NOT INSTALLED or UNKNOWN VERSION.
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 22.04.3 LTS
Platform: Linux-6.8.0-48-generic-x86_64-with-glibc2.35
Processor: x86_64
Machine: x86_64
Python version: 3.10.12
Process name: python
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: [popenfile(path='/home/dell/.config/Code/logs/20241113T073758/ptyhost.log', fd=37, position=0, mode='a', flags=33793), popenfile(path='/usr/share/code/resources/app/node_modules.asar', fd=38, position=51392, mode='r', flags=32768), popenfile(path='/usr/share/code/v8_context_snapshot.bin', fd=103, position=0, mode='r', flags=32768)]
Num physical CPUs: 6
Num logical CPUs: 12
Num usable CPUs: 12
CPU usage (%): [9.0, 8.3, 13.8, 8.9, 8.3, 54.5, 11.8, 12.4, 10.2, 9.0, 9.6, 54.1]
CPU freq. (MHz): 2614
Load avg. in last 1, 5, 15 mins (%): [13.1, 13.2, 15.6]
Disk usage (%): 14.4
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 15.4
Available memory (GB): 6.7
Used memory (GB): 8.1
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Printing GPU config...
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Num GPUs: 1
Has CUDA: True
CUDA version: 11.3
cuDNN enabled: True
NVIDIA_TF32_OVERRIDE: None
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None
cuDNN version: 8302
Current device: 0
Library compiled for CUDA architectures: ['sm_37', 'sm_50', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'compute_37']
GPU 0 Name: NVIDIA GeForce RTX 2060
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 30
GPU 0 Total memory (GB): 5.8
GPU 0 CUDA capability (maj.min): 7.5
Describe the bug
MetaTensor class has astype method. The expected behavior is to cast the data to e.g. the int type and obtain a MetaTensor. However, this function returns either torch.Tensor itself or np.array. In docs there isn't any metion about data type you will get as output. To achieve this you need to use convert_data_type function from monai.utils.
Expected behavior
Using astype method on metatensor needs to return metatensor or have additional argument output_type like in convert_data_type function.
Screenshots
Environment
python 3.10, pytorch 1.12.1