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Network Training

Code for training the network can be found in the following directories:

training/v2
python/depthmotionnet/v2
  • training/v2 contains the training script training.py.
  • python/depthmotionnet/v2 contains the definition of the network parts for version v2 (blocks.py) and loss functions (losses.py) as well as code for easy instantiating of the network.

Training Evolutions

The training process is made up of several stages called evolutions. To train DeMoN we use 6 evolutions (0_flow1, 1_dm1, 2_flow2, 3_flow2, 4_iterative, 5_refine). The instantiated and actively training network parts are visualized below: Training Evolutions

Prerequisites

  • The python library tfutils for managing the training evolutions must be downloaded and added to the python path (https://github.com/lmb-freiburg/tfutils.git)
  • The python directory in the demon root is added to the python path
  • lmbspecialops is built and added to the python path.
  • multivih5datareaderop is built (see readme)
  • Training data sets are available in the folder datasets/training_data. The script datasets/download_traindata.sh can be used to download the data sets.

The following script sets up all required libraries from scratch in a virtualenv demon_venv managed with pew:

pew new demon_venv # create new virtualenv
# the following commands are executed within the demon_venv virtualenv

# install python module dependencies
pip install tensorflow-gpu # or 'tensorflow' without gpu support
pip install pillow # for reading images
pip install matplotlib # required for visualizing depth maps
pip install Cython # required for visualizing point clouds
pip install h5py
pip install minieigen
pip install pandas
pip install scipy
pip install scikit-image
pip install xarray

# install tfutils library
git clone https://github.com/lmb-freiburg/tfutils.git
pew add $PWD/tfutils/python # add to python path

# clone demon repo with submodules
git clone --recursive https://github.com/lmb-freiburg/demon.git
DEMON_DIR=$PWD/demon
pew add $DEMON_DIR/python # add to python path

# build lmbspecialops
mkdir $DEMON_DIR/lmbspecialops/build
cd $DEMON_DIR/lmbspecialops/build
cmake .. # add '-DBUILD_WITH_CUDA=OFF' to build without gpu support
# (optional) run 'ccmake .' here to adjust settings for gpu code generation
make
pew add $DEMON_DIR/lmbspecialops/python # add to python path


# build multivih5datareaderop (requires OpenCV)
mkdir $DEMON_DIR/build 
cd $DEMON_DIR/build 
cmake ..
make

# download training data
cd $DEMON_DIR/datasets
./download_traindata.sh

Training Script

cd $DEMON_DIR/training/v2
pew in demon_venv 
python training.py

The training script creates the folder $DEMON_DIR/training/v2/training. Once training is complete the last snapshot can be found as $DEMON_DIR/training/v2/training/5_refine/checkpoints/snapshot-250000.*

The location of the training data can be adjusted in the file training.py.