Gradicon codestyle fix #16
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* asdf * beginto harmonize training proceedure * final training pipe * Rename preprocess_train_knees.py to preprocess_train_fullres_knees.py * Create preprocess_train_halfres_knees.py * details * brain unstripped * update old eval notebook Co-authored-by: Thomas Greer <tgreer@biag-w05.cs.unc.edu>
* cvpr start * Update TODO.md * Update TODO.md * Update TODO.md * Update TODO.md * Update and rename TODO.md to README.md * OAI eval script done * brain training script- testing if both steps in one script works * ugh * visualize training * training? * Update cvpr_network.py * don't say validation * normal log freq * Add train config for lung dataset. * fix shuffle name * HCP eval * progress * Adding support for different dimensions. * Fix the error " CPU tensor cannot be gathered" when using flips() for 2D and 1D data on 4 GPUS. * 1. Fix bug - GradientICONSparse running error when applied to 2D images. 2. Add framework parameter to train_two_stage so that we can run the training process on ICON as well. * Add ablation study script for comparing the training on different resolutions. * fix preprocess * script for COPDGene_eval * batch size + switching images * HCP ants eval * COPDGene_eval.py * OAI_ants_eval_needs_work * Get code for bending energy or velocity field ablation into the cvpr branch (#50) * Update network_wrappers.py * Update network_wrappers.py * Update network_wrappers.py * Update networks.py * Update network_wrappers.py * Update train.py * Update losses.py * name mistake * crimes * explain bizzare code in comment * Update network_wrappers.py * Update network_wrappers.py * Update network_wrappers.py * Update network_wrappers.py * Add learn2reg abdomenCTCT and NLST dataset helper function to icon data script. * Add train script for the learn2reg AbdomenCTCT registration task and NLST task. * Add copdgene train set to data script. * Add train script for network capacity and network structure ablation study. * evaluate OAI at half resolution to match prior work * Add experiment script for comparing convergence speed between icon and gradICON. * Add option to flips function so that it could print foldings in percentage. * Add evaluation script for learn2reg abdomenCTCT registration. * Fix the bug when normalize the intensity to [0,1]. * Fix bug: footsteps is initialized twice. Because utils initializes footsteps when imported. * Add evaluation script for learn2reg NLST task. * training scripts for abdomen and learn2reg lung * abdomen eval fixes * Add support for specifying output folder via argument list in network structure ablation study scripts. * folds * update comparison regularizers * Update losses.py (#51) * OAI_eval with torch.grid_sample * real grid_sample test * chunkin along * Asdfafdsa * synthmorph * synthmorph * Add HCP evaluation script for synthmorph. * Add folding computation into the script. * Fix Bending Energy. * Experiment of comparing regularizers with varying lambdas. * Plotting convergence speed comparison between ICON and GradICON. * Update requirements.txt * Update setup.cfg * Add model statistics computation. * Clean up the SynthMorph evaluation code. * Add test code for SynthMorph evaluation code. * Clean up the notebooks of the varying lambda experiments. * Add the reason of having a copy of VM UNet to the comments. * Add description of how to run the model statistics computation script. * Change the required itk version to 5.3.0 * Add the pretrained models to package. * Add test script for brain registration. * Fix bug: Should have used pre-trained model used in test_brain_itk. * Lossen the test criteria for brain registration so that the test case can pass when ran on cpu. * Fix the comments so that sphinx can generate documentation. * Unify the output of flips() function. Now the output should be a detached tensor. Co-authored-by: Lin Tian <lintian@cs.unc.edu> Co-authored-by: Raul <sonic1sonic@gmail.com>
* make sure we aren't scaling a signed short image to [0, 1) * enable cast * fix tests * add new module to doc
* Fix bug: input_channels was not truly reflecting the number of channels of x or y when given (x,y) as the input. * Remove input_channels argument in UNet2 to avoid potential error caused by inconsistency between the two arguments input_channels and channels. * Refactor all the similarity loss to inherient from SimilarityBase. SimilarityBase has a member variable called isInterpolated and it indicates whether the similarity loss class requires mask for the interpolated evaluation. * 1. Fix bug: Should check whether inbounds_tag is None or not. 2. Add assertion to check the shape of image_A and image_B. * Set correct shape for the inbounds_tag when images have multiple channels. * Refactor the test script according to the SimilarityBase class. * Refactor the test script according to the SimilarityBase class. * Refactor the test script according to the SimilarityBase class. * To allow using similarity measure defined by user. * Keep ssd and ssd_only_interpolated for backward compatibility. * Add itk interface for multi-modality registration task.
…arity meaure with isInterpolated set to True requires the inbounds_tag to be passed as one extra channel on image_A, otherwise the similarity measure will accept the two images with the same number of channels.
2.Add freesurfer affine evaluation script. 3.Move all the helper functions to helper.py. 4.Add a prepare script so that we process the image for Synthmorph once.
…ommit them to git.
…itialization error.
I don't think we meant to keep this check after switching to the "getattr" approach
Add GradICON paper and reference.
Use input image type instead of a predefined one. Also, refactoring to be compliant with PEP8 (79 characters max length)
vendor_gaussian_kernel
EHN: Change predefined image types for input images
This is the configuration we actually used in training
Reduce memory footprint
Update losses.py
* Update cpu-test-action.yml * Update setup.cfg * Update setup.cfg * Update setup.cfg * Update setup.cfg * Update setup.cfg * Update setup.cfg * Update setup.cfg * Update setup.cfg * Update cpu-test-action.yml * Update cpu-test-action.yml * Update setup.cfg * Update requirements.txt * Update setup.cfg * Update requirements.txt * Update setup.cfg * Update requirements.txt * Update setup.cfg * Update cpu-test-action.yml
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