seq_pianoroll

Lossless sequence compression on pianoroll datasets

Here we include the code for lossless sequential data compression on 4 pianoroll datasets: Nottingham, JSB, MuseData, and Piano-midi.de.

The experimental setup is adapted from the Filtering variational objectives (FIVO). We use the FIVO implementation (commit hash 8652bb3) and copy it in fivo/ for simplicity.

Usage

The reproduction pipeline consists of:

• building a Python 2 conda environment
• downloading and preprocessing the datasets
• training variational RNN models on the (training) datasets
• compressing the (test) datasets with the trained models

Build a Python 2 conda environment

To meet the dependency requirements of the FIVO repo, we recommend to create a new conda environment with Python 2 and TensorFlow.

conda env create -f fivo_environment.yml
conda activate fivo
cd ../..
pip install -e .

Download and preprocess the data

After downloading the pianoroll datasets, we truncate the sequences to a maximum length of 100 time steps as discussed in our paper. Note that the due to the truncation, we expect the outputs slightly different from the original FIVO paper.

export FIVO_CODEREPO=./fivo
export PIANOROLL_DATA=./datasets
export PIANOROLL_CKPT=./checkpoints
bash $PIANOROLL_DATA/create_pianorolls.sh $PIANOROLL_DATA $FIVO_CODEREPO

Training VRNN models

We train 3 models with ELBO, IWAE, and FIVO bounds on each dataset repectively and 4 particles are used. We provide the script for training the VRNN models with which you can simply run

bash train.sh 

Note that the numbers of updates are obtained by early stopping on a validation set. We also provide the pretrained checkpoints in checkpoints/. For more information about the API of the training routine, please refer to the original FIVO repo.

Evaluating trained models

Then you can evaluate the trained model on the test set in each setup with the provided script:

bash evaluate.sh

Compressing with trained models

To compress with trained models, a typical command is:

python compress_seq.py --config $PIANOROLL_DIR/compress_trunc_pianoroll.yml [ARGUMENT_LIST] 

The basic compressing arguments are specified in the config file configs/compress_trunc_pianoroll.yml and can be overridden by the [ARGUMENT_LIST] (see mcbits/argsparser.py for the argument list). The key arguments for compressing include:

• --dataset: the dataset used for compression
• --num_compress: the maximum number of sequences to compress (set to as large as 10000 to compress the whole test set)
• --decode_check: whether run decoding and check the correctness of decoded results
• --logdir: the model checkpoint used for compression
• --latent_size, --bound: the latent dimension and the variational bound specified at the training time
• --coder: the coder to use for compression (should fixed to SMCBitsBackCoder (BB-SMC) for this experiment)
• --num_particles: number of particles for compression
• --resample: whether apply resampling for BB-SMC. If False, BB-SMC reduces to BB-IS
• --adaptive: whether apply adaptive resampling for BB-SMC
• --lprec, --bprec: the precisions for the rANS stack
• --log_num_bucket, --prior_mprec, --prop_mprec, --cond_mprec: the precisions for discretizing the latent and observation distributions

We also provide a script for simply run compression with models trained in each setup:

bash compress.sh

The script compresses --num_compress=50 sequences for each setup and we provide results below for reference. Net bitrates (bits/sym) are compared.

Dataset	Musedata	Nottingham	JSB	Piano.
BB-ELBO	9.41	6.05	12.54	10.57
BB-IS (4)	9.43	4.96	11.90	10.52
BB-SMC (4)	8.66	4.87	10.96	10.37
Savings	8.0%	19.5%	12.6%	1.9%

Note that since the number of sequences compressed is relatively small, there maybe a large gap between the total bitrate and the net bitrate which decreases as --num_compress increases. Futhermore, the gap of BB-IS and BB-SMC is larger than that of BB-ELBO which can be addressed by the coupling technique as discussed in our paper, but we haven't implemented it for sequential coders.