This is code from the paper Stochastic Video Generation with a Learned Prior by Emily Denton and Rob Fergus. It is further updated for our task of anomaly detection for SM-MNIST dataset.

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.

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

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

This is the official PyTorch implementation of FutureGAN. The code accompanies the paper "FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs".

__Train the Network__<br>

$ python train.py --data_root='<path/to/trainsplit/of/your/dataset>'

If you want to display the training progress on Tensorboard, set the `--tb_logging` flag:

$ python train.py --data_root='<path/to/trainsplit/of/your/dataset>' --tb_logging=True

To resume training from a checkpoint, set `--use_ckpt=True` and specify the paths to the generator `ckpt_path[0]` and discriminator `ckpt_path[1]` like this:

$ python train.py --data_root='<path/to/trainsplit/of/your/dataset>' --use_ckpt=True --ckpt_path='<path_to_generator_ckpt>' --ckpt_path='<path_to_discriminator_ckpt>'

__Test and Evaluate the Network__<br>
To generate predictions with a trained FutureGAN, use the `--data_root` and `--model_path` flags to specify the path to your test data and generator weights and run:

$ python eval.py --data_root='<path/to/testsplit/of/your/dataset>' --model_path='<path_to_generator_ckpt>'

$ python eval.py --data_root='<path/to/testsplit/of/your/dataset>' --model_path='<path_to_generator_ckpt>' --metrics='mse' --metrics='psnr'