This repo contains an implementation of Generative Modeling by Estimating Gradients of the Data Distribution modified for use with imitation learning algorithms like noise conditioned energy based annealed rewards. The underlying algorithm is the same as in the original repo. It is however modified for use with demonstration motion data.

• PyTorch
• PyYAML
• tqdm
• pillow
• tensorboardX
• seaborn

main.py is the common gateway to all experiments. Type python main.py --help to get its usage description.

usage: main.py [-h] [--runner RUNNER] [--config CONFIG] [--seed SEED]
               [--run RUN] [--doc DOC] [--comment COMMENT] [--verbose VERBOSE]
               [--test] [--resume_training] [-o IMAGE_FOLDER]

optional arguments:
  -h, --help            show this help message and exit
  --runner RUNNER       The runner to execute
  --config CONFIG       Path to the config file
  --seed SEED           Random seed
  --run RUN             Path for saving running related data.
  --doc DOC             A string for documentation purpose
  --verbose VERBOSE     Verbose level: info | debug | warning | critical
  --test                Whether to test the model
  --resume_training     Whether to resume training
  -o IMAGE_FOLDER, --image_folder IMAGE_FOLDER
                        The directory of image outputs

The main runner class, used for annealed langevin dynamics is AnnealRunner.

• AnnealRunner The main runner class for experiments related to NCSN and annealed Langevin dynamics.

Configuration files are stored in configs/. For example, the configuration file of AnnealRunner is configs/anneal.yml. Log files are commonly stored in run/logs/doc_name, and tensorboard files are in run/tensorboard/doc_name. Here doc_name is the value fed to option --doc. However, if main.py is run coupled with other algorithms, a config can also be passed via the primary algorithm.

The usage of main.py is quite self-evident. For example, we can train an NCSN by running

python main.py --runner AnnealRunner --config anneal.yml --doc cifar10

Then the model will be trained according to the configuration files in configs/anneal.yml. The log files will be stored in run/logs/cifar10, and the tensorboard logs are in run/tensorboard/cifar10.

Suppose the log files are stored in run/logs/cifar10. We can produce samples to folder samples by running

python main.py --runner AnnealRunner --test -o samples