batchgenerators by MIC@DKFZ

batchgenerators is a python package that we developed at the Division of Medical Image Computing at the German Cancer Research Center (DKFZ) to suit all our deep learning data augmentation needs. It is not (yet) perfect, but we feel it is good enough to be shared with the community. If you encounter bug, feel free to contact us or open a github issue.

Suported Augmentations

We supports a variety of augmentations, all of which are compatible with 2D and 3D input data! (This is something that was missing in most other frameworks).

• Spatial Augmentations
  • mirroring
  • channel translation (to simulate registration errors)
  • elastic deformations
  • rotations
  • scaling
  • resampling
• Color Augmentations
  • brightness (additive, multiplivative)
  • contrast
  • gamma (like gamma correction in photo editing)
• Noise Augmentations
  • Gaussian Noise
  • Rician Noise
  • ...will be expanded in future commits
• Cropping
  • random crop
  • center crop
  • padding

Note: Stack transforms by using batchgenerators.transforms.abstract_transforms.Compose. Finish it up by plugging the composed transform into our multithreader: batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter

How to use it

The working principle is simple: Derive from DataLoaderBase class, reimplement generate_train_batch member function and use it to stack your augmentations! For simple example see batchgenerators/examples/example_ipynb.ipynb

We also now have an extensive example for BraTS2017/2018 with both 2D and 3D DataLoader and augmentations: batchgenerators/examples/brats2017/

There are also CIFAR10/100 datasets and DataLoader available at batchgenerators/datasets/cifar.py

Data Structure

The data structure that is used internally (and with which you have to comply when implementing generate_train_batch) is kept simple as well: It is just a regular python dictionary! We did this to allow maximum flexibility in the kind of data that is passed along through the pipeline. The dictionary must have a 'data' key:value pair. It optionally can handle a 'seg' key:vlaue pair to hold a segmentation. If a 'seg' key:value pair is present all spatial transformations will also be applied to the segmentation! A part from 'data' and 'seg' you are free to do whatever you want (your image classification/regression target for example). All key:value pairs other than 'data' and 'seg' will be passed through the pipeline unmodified.

'data' value must have shape (b, c, x, y) for 2D or shape (b, c, x, y, z) for 3D! 'seg' value must have shape (b, c, x, y) for 2D or shape (b, c, x, y, z) for 3D! Color channel may be used here to allow for several segmentation maps. If you have only one segmentation, make sure to have shape (b, 1, x, y (, z))

How to install locally

Install batchgenerators

pip install --upgrade batchgenerators

Import as follows

from batchgenerators.transforms.color_transforms import ContrastAugmentationTransform

Note: This package also includes 'generators'. Support for those will be dropped in the future. That was our old design.

Windows Support is very experimental!

Batchgenerators makes heavy use of python multiprocessing and python multiprocessing on windows is different from linux. To prevent the workers from freezing in windows, you have to guard your code with if __name__ == '__main__' and use multiprocessing's freeze_support. The executed script may then look like this:

# some imports and functions here

def main():
    # do some stuff

if __name__ == '__main__':
    from multiprocessing import freeze_support
    freeze_support()
    main()

This is not required on Linux.

Important!

Starting from version 1.14.6 numpy has issues with multiprocessing (it's supposed to be a feature...). Mutrix multiplications (which we are using to rotate coordinate systems for data augmentation) now run mutlithreaded on all available threads. This can cause chaos if you are using a multiprocessing pipeline, beacause each background worker will spawn a lot of threads to do the matrix multiplication (8 workers on a 16 Core machine = up to 8*16=256 threads. duh.). There is nothing we (dkfz devs) can do to tackle that problem, but this will only be a real issue in very specific configurations of data augmentation. If you notice unnecessarily high CPU load, there are two things you can do:

• (recommended) run all your experiments with OMP_NUM_THREADS=1 or (even better) add this to your environment variables (export OMP_NUM_THREADS=1 in .bashrc on linux)
• downgrade numpy to 1.14.5 (pip install numpy==1.14.5) to solve the issue

Numpy devs are aware of this problem and trying to find a solution (see numpy/numpy#11826 (comment))

Release Notes

• 0.19:

  • There is now a complete example for BraTS2017/8 available for both 2D and 3D. Use this if you would like to get some insights on how I (Fabian) do my experiments
  • Windows is now supported! Thanks @justusschock for your support!
  • new, simple parametrization of elastic deformation. Use SpatialTransform_2!
  • CIFAR10/100 DataLoader are now available for your convenience
  • a bug in MultiThreadedAugmenter that could interfere with reproducibility is now fixed

• 0.18:

  • all augmentations (there are some exceptions though) are implemented on a per-sample basis. This should make it easier to use the augmentations outside of the Transforms of batchgenerators
  • applicable Transforms now have a keyword p_per_sample with which the user can specify a probability with which this transform is applied to a sample. Before, this was handled by RndTransform and applied to the whole batch (so either all samples were augmented or none). Now this decision is made on a per-sample basis and increases variability by a lot.
  • following the previous point, RndTransform is now deprecated
  • AlternativeMultiThreadedAugmenter is now deprecated as well (no need to have this anymore)
  • pytorch users can now transform numpy arrays to pytorch tensors within batchgenerators (NumpyToTensor). For some reason, inter-process communication is faster with tensors (~factor 4), so this is recommended!
  • if numpy arrays were converted to pytorch tensors, MultithreadedAugmenter now allows to pin the memory as well (pin_memory=True). This will happen in a background thread (inspired by pytorch DataLoader). pinned memory can be copied to the GPU much faster. My (Fabian) classification experiment with Resnet50 got a speed boost of 12% from just that.