/lmbspecialops

A collection of tensorflow ops

Primary LanguageC++GNU General Public License v3.0GPL-3.0

lmbspecialops

License

lmbspecialops is a collection of tensorflow ops. The ops focus on networks for predicting depth and camera motion as in DeMoN, but many can also be useful for other tasks.

If you use this code for research please cite:

@InProceedings{UZUMIDB17,
  author       = "B. Ummenhofer and H. Zhou and J. Uhrig and N. Mayer and E. Ilg and A. Dosovitskiy and T. Brox",
  title        = "DeMoN: Depth and Motion Network for Learning Monocular Stereo",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
  month        = " ",
  year         = "2017",
  url          = "http://lmb.informatik.uni-freiburg.de//Publications/2017/UZUMIDB17"
}

See the Op documentation for a description of all functions.

Requirements

Building and using lmbspecialops depends on the following libraries and programs

tensorflow 1.4.0
cmake 3.7.1
python 3.5
cuda 8.0.61 (required for building with gpu support)

The versions match the configuration we have tested on an ubuntu 16.04 system. lmbspecialops can work with other versions of the aforementioned dependencies, e.g. tensorflow 1.3, but this is not well tested.

Build instructions

Checkout the repository and setup the build directory.

git clone https://github.com/lmb-freiburg/lmbspecialops.git
cd lmbspecialops

mkdir build
cd build
cmake ..
# cmake .. -DBUILD_WITH_CUDA=OFF # to disable gpu support

Use ccmake to customize the configuration. You may want to adapt GPU code generation to your graphics card. E.g. to enable code generation for the Tesla K80 enable GENERATE_KEPLER_SM37_CODE

ccmake .

Build the library

make

To use the ops, you need to add the lmbspecialops/python directory to your python path. Then you can import and use the ops like this

import lmbspecialops
import tensorflow as tf
import numpy as np

tf.InteractiveSession()

A = tf.constant([1,2,np.nan])
B = lmbspecialops.replace_nonfinite(A)
print(B.eval()) # prints [1, 2, 0]

Virtualenv build

To build inside a virtualenv make sure to activate the environment before running cmake.

If you encounter problems with cmake not finding the correct paths to your python interpreter or tensorflow see https://gist.github.com/datagrok/2199506 for possible solutions.

License

lmbspecialops is under the GNU General Public License v3.0