Project-MONAI / Project-MONAI/MONAI
PHL filtering with more than 16 channels
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Feature request
- Dominant language
- Python
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Description
Hello,
I am trying to play with the CRF block. Here is the current error message I got:
File "/home/bastien/.virtualenvs/cbct-z52AAXGJ/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/bastien/.virtualenvs/cbct-z52AAXGJ/lib/python3.8/site-packages/monai/networks/blocks/crf.py", line 97, in forward
bilateral_output = PHLFilter.apply(output_tensor, bilateral_features)
File "/home/bastien/.virtualenvs/cbct-z52AAXGJ/lib/python3.8/site-packages/monai/networks/layers/filtering.py", line 93, in forward
output_data = _C.phl_filter(input, scaled_features)
RuntimeError: PHL filtering not implemented for channel count > 16
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Is there any plan to increase this?
Thank you!
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading monai/networks/blocks/crf.py around the PHLFilter call and monai/networks/layers/filtering.py where _C.phl_filter is invoked. Trace the implementation behind that binding and its channel-count limitation. Done means CRF PHL filtering accepts inputs with more than 16 channels and the existing behavior remains intact.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100