kristofbc / physical-interaction-video-prediction

Based on Chealsea Finn's et al "Unsupervised Learning for Physical Interaction through Video Prediction"

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Unsupervised Learning for Physical Interaction through Video Prediction

Based on the paper from C. Finn, I. Goodfellow and S. Levine: "Unsupervised Learning for Physical Interaction through Video Prediction". Implemented in Chainer.

Creating the data need for training

$ sh data/raw/download_data.sh # Will download all the data from Google's ftp to data/raw
$ make data # Will create the processed data available in data/processed

Running the training process

$ make train
$ make ARGS="--max_batchsize=64" train # with arguments

Running the prediction process

$ make predict MODEL_DIR={FOLDER_NAME_IN_/MODELS} MODEL_NAME={NAME_OF_THE_MODEL_IN_MODEL_DIR} DATA_INDEX={INDEX_OF_GROUND_TRUTH_IN_DATA_DIR}
$ make predict MODEL_DIR=20170630-181202-CDNA-32 MODEL_NAME=training-228 DATA_INDEX=0 # i.e

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Based on Chealsea Finn's et al "Unsupervised Learning for Physical Interaction through Video Prediction"

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