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Learning to Generate Compositional Color Descriptions

Code and supplementary material

Note that this repo is split off from a larger project still in development: https://github.com/futurulus/coop-nets

Outputs tables

A full table of samples from the model that provided the examples in Table 1 and Table 3 is included at:

outputs/color_samples.html

Dependencies

You'll first need Python 2.7. Creating and activating a new virtualenv or Anaconda environment is recommended. Then run this script to download data and Python package dependencies:

./dependencies

The dependencies script is reasonably simple, so if this fails, it should be possible to look at the script and manually perform the actions it specifies.

This code is written to be run on a Linux system; we've also tested it on Mac OS X (but see "Troubleshooting": missing g++ will cause the program to run impossibly slowly). The code is unlikely to run on Windows, but you're welcome to try.

Usage

The easiest way to see the model in action is to use the colordesc module:

>>> import colordesc
>>> describer = colordesc.ColorDescriber()
>>> describer.describe((255, 0, 0))
'red'

This loads a pickled Theano model, which may not be very robust to different system configurations or very future-proof; if you run into problems with this, read on.

Repickling from model params

The pickle files in the models directory, with the exception of lstm_fourier_quick.p, contain only the parameters of the model, which makes them more portable, but they require a bit more work:

$ mkdir -p runs/lstm_fourier
$ cp models/lstm_fourier.p runs/lstm_fourier/model.p
$ python quickpickle.py --config models/lstm_fourier.config.json --run_dir runs/lstm_fourier
$ python
>>> import colordesc, cPickle as pickle
>>> with open('runs/lstm_fourier/quickpickle.p', 'rb') as picklefile:
...     describer = colordesc.ColorDescriber(picklefile)
>>> describer.describe((255, 0, 0))
'red'

Running experiments

To re-run the experiments from the paper (Table 2) with pre-trained models, use the following command, where lstm_fourier (our best model) can be replaced with any of the eight experiment configurations in the outputs/ directory:

python run_experiment.py --config models/lstm_fourier.config.json \
                         --load models/lstm_fourier.p \
                         --progress_tick 10

This should take about 15 minutes on a typical new-ish machine. Look for these metrics in the outputs to compare with Table 2:

dev.perplexity.gmean
dev.aic.sum
dev.accuracy.mean

The results of the experiment, including predictions and log-likelihood scores, will be logged to the directory

runs/lstm_fourier

To retrain a model from scratch, supply only the config file:

python run_experiment.py --config models/lstm_fourier.config.json

Note that this may require several days to train on CPU. A properly-configured GPU usually takes a few hours and can be used by passing --device gpu0. See

http://deeplearning.net/software/theano/tutorial/using_gpu.html

for necessary configuration.

Troubleshooting

  • Error messages of the form

    error: argument --...: invalid int value: '<pyhocon.config_tree.NoneValue object at ...>'

    should be solved by making sure you're using pyhocon version 0.3.18; if this doesn't work, supplying a number for the argument should fix it. We've seen this with the arguments --train_size or --test_size; to fix these, add

    --train_size 10000000 --test_size 10000000

  • A warning message of the form

    WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.

    should be heeded. Otherwise even just running prediction will take a very long time (days). Check whether you can run g++ from a terminal, or try changing the Theano cxx flag (in ~/.theanorc) to point to an alternative C++ compiler on the system.

  • If retrying a run after a previous error, you'll need to add the option --overwrite (or specify a different output directory with --run_dir DIR). The program will remind you of this if you forget.

  • Very large dev perplexity (dev.perplexity.gmean > 50) could indicate incompatible changes in the version of Lasagne or Theano (we've seen this with Lasagne 0.1). We've reproduced our main results using the development versions of Theano and Lasagne as of June 2, 2016: