Skip to main content

PyTorch bindings for PYRO-NN (https://github.com/csyben/PYRO-NN)

Project description

PyPI version https://travis-ci.org/theHamsta/pyronn-torch.svg?branch=master

pyronn-torch

This repository provides PyTorch bindings for PYRO-NN, a collection of back-propagatable projectors for CT reconstruction.

Feel free to cite our publication:

@article{PYRONN2019,
author = {Syben, Christopher and Michen, Markus and Stimpel, Bernhard and Seitz, Stephan and Ploner, Stefan and Maier, Andreas K.},
title = {Technical Note: PYRO-NN: Python reconstruction operators in neural networks},
year = {2019},
journal = {Medical Physics},
}

Installation

From PyPI:

pip install pyronn-torch

From this repository:

git clone --recurse-submodules --recursive https://github.com/theHamsta/pyronn-torch.git
cd pyronn-torch
pip install torch
pip install -e .

You can build a binary wheel using

python setup.py bdist_wheel

Usage

import pyronn_torch

#ConeBeamProjector(volume_shape,
#                  volume_spacing,
#                  volume_origin,
#                  projection_shape,
#                  projection_spacing,
#                  projection_origin,
#                  projection_matrices)
projector = pyronn_torch.ConeBeamProjector(
    (128, 128, 128),
    (2.0, 2.0, 2.0),
    (-127.5, -127.5, -127.5),
    (2, 480, 620),
    [1.0, 1.0],
    (0, 0),
    np.array([[[-3.10e+2, -1.20e+03,  0.00e+00,  1.86e+5],
               [-2.40e+2,  0.00e+00,  1.20e+03,  1.44e+5],
               [-1.00e+00,  0.00e+00,  0.00e+00,  6.00e+2]],
              [[-2.89009888e+2, -1.20522754e+3, -1.02473585e-13,
                1.86000000e+5],
               [-2.39963440e+2, -4.18857765e+0,  1.20000000e+3,
                1.44000000e+5],
               [-9.99847710e-01, -1.74524058e-2,  0.00000000e+0,
                6.00000000e+2]]]) # two projection matrices
)
projection = projector.new_projection_tensor(requires_grad=True)

projection += 1.
result = projector.project_backward(projection, use_texture=True)

assert projection.requires_grad
assert result.requires_grad

loss = result.mean()
loss.backward()

Or easier with PyCONRAD (pip install pyconrad)

projector = pyronn_torch.ConeBeamProjector.from_conrad_config()

The configuration can then be done using CONRAD (startable using conrad from command line)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pyronn-torch, version 0.2.2
Filename, size File type Python version Upload date Hashes
Filename, size pyronn-torch-0.2.2.tar.gz (42.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page