Attention-UNet implementation does not propagate kernel_size #7726
Description
Description
When changing the kernel_size of the attention-unet, amount of trainable parameters stays the same.
Reason: The kernel size will be set to the assigned default value, because the when creating ConvBlocks the Kernelsize is not propagated.
To Reproduce
Steps to reproduce the behavior:
- Create a model "Attention Unet" from monai.networks.nets
from monai.networks.nets import AttentionUnet
model = AttentionUnet(
spatial_dims = 2,
in_channels = 1,
out_channels = 1,
channels = (2, 4, 8, 16),
strides = (2,2,2),
kernel_size = 5,
up_kernel_size = 5
)
- Run command
from torchinfo import summary
summary(model, (1,1,16,16))
Expected behavior
Total params: 18,846
Trainable params: 18,846
Non-trainable params: 0
Total mult-adds (M): 0.37
Actual behavior
Total params: 10,686
Trainable params: 10,686
Non-trainable params: 0
Total mult-adds (M): 0.27
Environment
python -c "import monai; monai.config.print_debug_info()"
MONAI version: 1.3.0
Numpy version: 1.24.4
Pytorch version: 1.9.0+cu111
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 865972f7a791bf7b42efbcd87c8402bd865b329e
MONAI __file__: /opt/conda/lib/python3.8/site-packages/monai/__init__.py
Optional dependencies:
Pytorch Ignite version: 0.4.11
ITK version: 5.3.0
Nibabel version: 5.2.1
scikit-image version: 0.18.1
scipy version: 1.10.1
Pillow version: 8.2.0
Tensorboard version: 2.5.0
gdown version: 4.7.3
TorchVision version: 0.10.0+cu111
tqdm version: 4.61.1
lmdb version: 1.2.1
psutil version: 5.8.0
pandas version: 1.2.5
einops version: 0.3.0
transformers version: 4.8.1
mlflow version: 2.12.1
pynrrd version: 1.0.0
clearml version: 1.15.1
Additional remarks
This behaviour might occur for other network architectures aswell.
Please review this issue for similar architectures (I have not checked this myself).