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deepfigures-open

Figure extraction using deep neural nets.

deepfigures-open is the companion code to the paper Extracting Scientific Figures with Distantly Supervised Neural Networks. It provides code to run our model and extract figures from PDFs, as well as code for generating our training data. The generated dataset used in our paper is available for download here.

Note: This is research code and is not intended for use in production.

Setup: Running the Model

Compile pdffigures2

Deepfigures depends on pdffigures2 for caption extraction. You must compile the utility and place it into the bin/ directory:

git clone https://github.com/allenai/pdffigures2
cd pdffigures2
sbt assembly
mv target/scala-2.11/pdffigures2-assembly-0.0.12-SNAPSHOT.jar ../bin
cd ..
rm -rf pdffigures2

If the jar for pdffigures has a different name then 'pdffigures2-assembly-0.0.12-SNAPSHOT.jar', then adjust the PDFFIGURES_JAR_NAME parameter in deepfigures/settings.py accordingly.

Download Weights for the Model

You have to download weights for the deepfigures model into this repository in order to run it. You can download a tarball of the weights here. Once you've downloaded the tarball, extract it and place the weights/ directory in the root of this repository.

If you choose to name the weights directory something different, be sure to update the TENSORBOX_MODEL constant in deepfigures/settings.py.

Setup: Generating Training Data

Set Arxiv Data Directories

In deepfigures/settings.py set the ARXIV_DATA_TMP_DIR and ARXIV_DATA_OUTPUT_DIR variables to local directories on your machine. Make sure that these directories have at least a few TBs of storage since there are a lot of arXiv papers.

Set the Pubmed Data Directories

In deepfigures/settings.py set the PUBMED_INPUT_DIR, PUBMED_INTERMEDIATE_DIR, PUBMED_DISTANT_DATA_DIR, and LOCAL_PUBMED_DISTANT_DATA_DIR to different directories.

PUBMED_INPUT_DIR, PUBMED_INTERMEDIATE_DIR, and PUBMED_DISTANT_DATA_DIR can be directories in S3, but LOCAL_PUBMED_DISTANT_DATA_DIR should be a local directory.

Additionally, PUBMED_INPUT_DIR should have all of the Pubmed Open Access subset papers split into directories with the following structure:

xx/yy/example-pmc-data.tar.gz

Where xx and yy range from 00 to ff.

Install Dependencies

Make sure you have docker installed and that you also have all the requirements installed:

pip install -r requirements.txt

AWS Integration

Much of the functionality for this code requires usage of AWS (such as downloading the data for arxiv). Make sure the deepfigures-local.env file is filled out with your AWS credentials if you want to run with this functionality. Please note that running this code with the AWS functionality will incur charges on your AWS account.

The AWS integration is used for:

  • downloading the arXiv data dump from S3 to generate the arXiv paper labels.
  • storing intermediate computations in S3 while running the pubmed data pipeline.

For most use cases, users will prefer to download the dataset directly rather than rebuilding it themselves.

Using the Library

Use the manage.py script in the root of this repository to view common commands for development. To get a list of commands, run:

python manage.py --help

You'll see something like:

$ python manage.py --help
Usage: manage.py [OPTIONS] COMMAND [ARGS]...

  A high-level interface to admin scripts for deepfigures.

Options:
  -v, --verbose        Turn on verbose logging for debugging purposes.
  -l, --log-file TEXT  Log to the provided file path instead of stdout.
  -h, --help           Show this message and exit.

Commands:
  build           Build docker images for deepfigures.
  detectfigures   Run figure extraction on the PDF at PDF_PATH.
  generatearxiv   Generate arxiv data for deepfigures.
  generatepubmed  Generate pubmed data for deepfigures.
  testunits       Run unit tests for deepfigures.

To learn more about a command, call it with the --help option.

To extract figures from a PDF, use the detectfigures command.

Contact

For questions, contact the authors of the paper Extracting Scientific Figures with Distantly Supervised Neural Networks.