Skip to content

Latest commit

 

History

History
45 lines (40 loc) · 2.44 KB

README.md

File metadata and controls

45 lines (40 loc) · 2.44 KB
Raw

Stochastic Video Generation with a Learned Prior

This is code for the paper Stochastic Video Generation with a Learned Prior by Emily Denton and Rob Fergus. See the project page for details and generated video sequences.

Training on Stochastic Moving MNIST (SM-MNIST)

To train the SVG-LP model on the 2 digit SM-MNIST dataset run:

python train_svg_lp.py --dataset smmnist --num_digits 2 --g_dim 128 --z_dim 10 --beta 0.0001 --data_root /path/to/data/ --log_dir /logs/will/be/saved/here/

If the MNIST dataset doesn't exist, it will be downloaded to the specified path.

BAIR robot push dataset

To download the BAIR robot push dataset run:

sh data/download_bair.sh /path/to/data/

This will download the dataset in tfrecord format into the specified directory. To train the pytorch models, we need to first convert the tfrecord data into .png images by running:

python data/convert_bair.py --data_dir /path/to/data/

This may take some time. Images will be saved in /path/to/data/processeddata. Now we can train the SVG-LP model by running:

python train_svg_lp.py --dataset bair --model vgg --g_dim 128 --z_dim 64 --beta 0.0001 --n_past 2 --n_future 10 --channels 3 --data_root /path/to/data/ --log_dir /logs/will/be/saved/here/

To generate images with a pretrained SVG-LP model run:

python generate_svg_lp.py --model_path pretrained_models/svglp_bair.pth --log_dir /generated/images/will/save/here/

KTH action dataset

First download the KTH action recognition dataset by running:

sh data/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. The following script will do the conversion, but beware, it's written in lua (sorry!):

th data/convert_kth.lua --dataRoot /my/kth/data/path/ --imageSize 64

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. To train the SVG-FP model on 64x64 KTH videos run:

python train_svg_fp.py --dataset kth --image_width  64 --model vgg --g_dim 128 --z_dim 24 --beta 0.000001 --n_past 10 --n_future 10 --channels 1 --lr 0.0008 --data_root /path/to/data/ --log_dir /logs/will/be/saved/here/