This repository contains code for running experiments from the following paper:

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks.
Eric Crawford and Joelle Pineau.
AAAI (2019).

This repository and the companion repository dps are both likely to undergo further development in the future. In general, we will attempt to keep the experiments from the paper runnable, but in case something breaks, one can always check out the aaai_2019 branches of both repositories to obtain the code as it was for the paper. Also, branches for both repos named aaai_2019_v1 preserve most of the behaviour of the original experiments, but with significant code improvements.

1. Install tensorflow with GPU support. The branch aaai_2019 can work with any version of tensorflow >= 1.4, but has not been tested extensively with any version other than 1.8. The more recent branches (master and aaai_2019_v1) require a relatively recent version of tensorflow_probability (>= 0.2) and so require tensorflow >= 1.9.

   If using branch master or aaai_2019_v1, also install a version of tensorflow_probability that is compatible with your version of tensorflow (compatibilities can be found here).

   For example, in recent development I've been using tensorflow==1.13.1 and tensorflow-probability==0.6.0.

2. Clone dps, optionally switch to aaai_2019 or aaai_2019_v1 branches, and install:

   git clone https://github.com/e2crawfo/dps.git
   cd dps
   (optional: git checkout aaai_2019 or git checkout aaai_2019_v1)
   pip install -r requirements.txt
   pip install -e .
   cd ..
   

3. Clone auto_yolo, optionally switch to aaai_2019 or aaai_2019_v1 branches, and install:

   git clone https://github.com/e2crawfo/auto_yolo.git
   cd auto_yolo
   (optional: git checkout aaai_2019 or git checkout aaai_2019_v1)
   pip install -r requirements.txt
   pip install -e .
   cd ..
   

4. Compile custom tensorflow ops resampler_edge and render_sprites.

   cd auto_yolo/auto_yolo/tf_ops/resampler_edge && make
   cd ../render_sprites && make
   cd ../../../../
   

5. Setup scratch directory and download emnist data.

   cd dps/scripts
   python download.py emnist --shape=14,14
   

   The last line should first ask you for a scratch directory on your computer. It will then download emnist data into that directory, and reshape it to (14, 14) (this process can take a while).

To train SPAIR on a scattered MNIST dataset:

cd auto_yolo/experiments/comparison
python yolo_air_run.py local