README.md

IODINE.pytorch

Unofficial PyTorch implementation of IODINE https://arxiv.org/abs/1903.00450

UPDATE: Please see the code for our recent ICML paper on EfficientMORL, a hierarchical VAE that trains/test faster and can use less memory than IODINE https://github.com/pemami4911/EfficientMORL

Installation

We provide a conda environment file. Install dependencies with conda env create -f multi_object.yml.

Data

The official Google Multi-Object Datasets are available as TFRecords here: https://github.com/deepmind/multi_object_datasets. This code uses h5py versions of this data that I created by modifying the TFRecords, available at this Dropbox link for download.

Training

First create the following in a desired directory (with sufficient storage for holding model weights and tensorboard logs):

experiments/
    weights/
    tb/
    runs/

Hyperparameters and runtime parameters can be modified in train_params.json. To train the model, use the following command:

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 train_distributed.py with train_params.json dataset.data_path=$PATH_TO_DATASET_DIR seed=$SEED training.batch_size=8  training.out_dir=$DIR_TO_STORE_OUTPUTS training.run_suffix=$RUN_NAME --file_storage $DIR_TO_STORE_OUTPUTS/runs

For more GPUs, include their GPU IDs in the CUDA_VISIBLE_DEVICES and increase nproc_per_node to be equal to the number of GPUs.

Evaluation

Compute the ARI score for 320 test images.

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 eval_distributed.py with eval_params.json dataset.data_path=$PATH_TO_DATASET_DIR seed=$SEED test.checkpoint_dir=$DIR_WHERE_MODEL_WEIGHTS_ARE_SAVED test.checkpoint=$SAVED_MODEL_NAME