rszeto / drnet

DrNet

[paper] [project page]

Torch implementation for Unsupervised Learning of Disentangled Representations from Video.

Training

To train the base model run:

th train_drnet.lua 

or the model with skip connections between content encoder and decoder:

th train_drnet_skip.lua 

To train an LSTM on the pose vectors run:

th train_lstm.lua --modelPath /path/to/model/

Training on KTH

First download the KTH action recognition dataset by running:

sh datasets/download_kth.sh /my/kth/data/path/

where /my/kth/data/path/ is the directory the data will be downloaded into. Next, convert the downloaded .avi files into .png's for the data loader. To do this you'll want ffmpeg installed. Then run:

th convert_kth --dataRoot /my/kth/data/path/ --imageSize 128

The --imageSize flag specifiec the image resolution. Experimental results in the paper used 128x128, but you can also train a model on 64x64 and it will train much faster. Now you're ready to train the DrNet model by running:

th train_drnet_skip --dataRoot /my/kth/data/path/ --imageSize 128 --nThreads 2

Setting --nThreads utilizes multithreaded data loading and will speed up training significantly.