D04C0Mn1Ce419

Synopsis

1. Github Download Commit As Patch
2. Github Download Commit As Patch
3. Github Commit To Master
4. Github Api Commit History
5. Github Download Commit As Patch

Submission for Multimodal Brain Tumor Segmentation Challenge 2017 (http://braintumorsegmentation.org/). A patch-based 3D U-Net model is used. Instead of predicting the class label of the center pixel, this model predicts the class label for the entire patch. A sliding-window method is used in deployment with overlaps between patches to average the predictions.

Kirikiroid2_patch - Patch Library for Kirikiroid2. Skip to content. Download GitHub Desktop and try again. Latest commit cf82b6d May 6, 2018. Jan 30, 2011  Some time ago, the brilliant GitHub people gave me the following tip. Rajarshi is lazy, and might find it interesting.By appending.patch to the commit URL, a commit can easily be downloaded as patch.

Code Example

The workflow includes bias correction, patch extraction, training, post-processing, testing and submission.
After training data is downloaded, run python bias_correction.py input_dir to perform bias field correction based on N4ITK (https://www.ncbi.nlm.nih.gov/pubmed/20378467). The corrected dataset will be saved at the same folder with the raw dataset.
Run python generate_patches.py input_dir output_dir to generate patches for training.
To train the model, run python main.py --train=True --train_data_dir=train_patch_dir. Or you can modify the default parameters in main.py so that you can just run python main.py. Check model.py for more details about the network structure.
To test the model on validation dataset, run python main.py --train=False --deploy_data_dir=deploy_data_dir --deploy_output_dir=deploy_output_dir. The results will be saved at deploy_output_dir. The network structure for survival prediction is not working good as the result is similar as random guessing. So you can ignore that by setting run_survival to False.
To combine the results and generate the final label maps, run python prepare_for_submission.py input_dir output_dir.

Installation

The model is implemented and tested using python 2.7 and Tensorflow 1.1.0, but python 3 and newer versions of Tensorflow should also work.Other required libraries include: numpy, h5py, skimage, transforms3d, nibabel, scipy, nipype. You also need to install ants for bias correction. Read the instructions for Nipype (http://nipy.org/nipype/0.9.2/interfaces/generated/nipype.interfaces.ants.segmentation.html) and Ants (http://stnava.github.io/ANTs/) for more information.

Contributors

Xue Feng, Department of Biomedical Engineering, University of Virginiaxf4j@virginia.edu

I have a git repo, where we apply many patches in test environment.

If I have to remove the patch and apply a new one, at present I clone the live content and reapply all the test patches and push again.This process is somehow cumbersome and also leads to errors at times I also miss one or two patches.

Github Download Commit As Patch

I wanted to know if there is a way to remove a patch and apply the new one

Also, to add one way is there if we commit each time to the patch and then i can use:

The above does not work for me at all times.

1 Answer

TL;DR

You can revert a patch with:

Github Download Commit As Patch

You can generate a patch either by one of the following:

Github Commit To Master

This will generate a patch from a diff

If you want to generate a patch for just the HEAD commit:

Github Api Commit History

You can generate a patch for the previous 3 commits from HEAD:

You can apply the patch by:

You can revert a patch with:

When you generate a patch it is just a diff with metadata; files, line numbers adds/removes; something along the following:

Github Download Commit As Patch

So when you use git apply you're essentially applying the edits as per to the tree.

When you then run git apply -R git will simply do the opposite to the patch.

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