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Codecov Report
@@ Coverage Diff @@
## main #156 +/- ##
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+ Coverage 80.80% 81.29% +0.48%
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Files 72 74 +2
Lines 5320 5480 +160
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+ Hits 4299 4455 +156
- Misses 1021 1025 +4
Continue to review full report at Codecov.
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@wdika Can you rebase from main? |
Co-authored-by: wdika <dimitriskarkalousos@gmail.com>
@jonasteuwen I rebased.
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# Conflicts: # direct/__init__.py # direct/data/datasets.py # direct/data/lr_scheduler.py # direct/data/mri_transforms.py # direct/data/transforms.py # direct/engine.py # direct/functionals/psnr.py # direct/nn/didn/didn.py # direct/nn/unet/unet_2d.py # direct/utils/io.py # docker/README.md # docs/model_zoo.rst # setup.cfg
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@wdika Can you pull from main please? Black is failing because there is a new version |
Thanks @georgeyiasemis! I had to update Black, which now passes successfully. |
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@wdika Codacy also complains about some issues: https://app.codacy.com/gh/NKI-AI/direct/pullRequest?prid=8627324 Can you take a look please? |
@georgeyiasemis thanks for the reply, I fixed the unused imports and variables errors. However, there are still four left about "Line too long" and "Too many local variables". After checking the files of the rest implemented models I saw that those are common errors. So, could you please let me know if this would pass the standards now? |
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@wdika Can you please rebase from main and make sure all tests pass? |
@jonasteuwen I rebased from main. Still, the PR Labeler doesn't pass because of access restrictions. Codacy also complains but I guess those are in general "errors" of direct. Such as the "Line too long" when citing a paper and "Too many local variables" in the forward func of the model engine. As I see compared to the rest implementations those errors are present there as well. Not sure if I need to change something there. |
* Adds CIRIM Co-authored-by: wdika <dimitriskarkalousos@gmail.com>
* Adds CIRIM Co-authored-by: wdika <dimitriskarkalousos@gmail.com>
This pr adds the Cascades of Independently Recurrent Inference Machines (CIRIM), as presented in "Karkalousos, D. et al. (2021) ‘Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction’. Available at: https://arxiv.org/abs/2111.15498v1".