This repo contains my solution code for the Benetech - Making Graphs Accessible competition. A detailed summary of the solution is posted here. Please refer to the following sections for details on training, inference and dependencies. If you run into any issues with the setup/code or have any questions/suggestions, please feel free to contact me at [email protected]. Thanks!
I used the computing resources from Jarvislabs.ai. Specifically, models were trained on the following instance:
- Ubuntu 20.04.5 LTS (128 GB boot disk)
- Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz (7 vCPUs)
- 1 x NVIDIA A100 40GB GPU OR 1 x NVIDIA A6000 48GB GPU
I used PyTorch-2.0 image from Jarvislabs.ai, which comes with:
- Python 3.10.11
- CUDA 11.8
- Python packages installation:
pip install -r requirements.txt
Please make sure Kaggle API is installed. Then run the following script to download the required datasets:
chmod +x ./setup.sh
./setup.sh
Please note that the above script will create a datasets folder in the directory located one level above the current directory. The external datasets will be downloaded in the datasets folder. After executing the above script, the datasets folder should have the following structure:
./processed
./processed/fold_split
./processed/deps
./processed/deps/mga_textures_cc
./processed/deps/mga_textures_cc/mga_textures_cc
./processed/synthetic
./processed/synthetic/annotations
./processed/synthetic/images
./processed/mga_pl
./processed/mga_pl/annotations
./processed/mga_pl/images
./processed/mga_icdar
./processed/mga_icdar/annotations
./processed/mga_icdar/images
./benetech-making-graphs-accessible
./benetech-making-graphs-accessible/test
./benetech-making-graphs-accessible/test/images
./benetech-making-graphs-accessible/train
./benetech-making-graphs-accessible/train/annotations
./benetech-making-graphs-accessible/train/images
The training scripts and configurations are located in the code and conf folder respectively. The training pipeline is implemented using HuggingFace's transformers, datasets and accelerate libraries. The training comprises of two phases: domain adaptation and specialization.
In the first phase, I leveraged a large number of synthetic plots to adapt the google/matcha-base backbone for the current task.
HYDRA_FULL_ERROR=1 python ./code/train_r_final.py \
--config-name conf_r_final \
fold=0 \
use_wandb=false \
debug=false
Expected training time: 50 hours (A6000 GPU) OR 36 hours (A100 GPU)
In the second phase, I used over-sampled extracted / non-generated plots to specialize the pipeline for real world graphs. In this phase, I created separate models for scatter and non-scatter plots, primarily to boost performance of scatter plot subset. The checkpoint from the domain adaptation phase is used as the starting point for the specialization phase.
Training for scatter plot subset is implemented using the following script:
HYDRA_FULL_ERROR=1 python ./code/train_r_scatter.py \
--config-name conf_r_scatter \
fold=0 \
use_wandb=false \
debug=false
Training for non-scatter plot subset is implemented using the following script:
HYDRA_FULL_ERROR=1 python ./code/train_r_rest.py \
--config-name conf_r_rest \
fold=0 \
use_wandb=false \
debug=false
The inference notebook is published here: https://www.kaggle.com/code/conjuring92/a05-mga-split-pipe
The notebook uses three trained model checkpoints which are hosted as kaggle datasets:
- https://www.kaggle.com/datasets/conjuring92/mga-r-final-matcha-v2-ft-i2
- https://www.kaggle.com/datasets/conjuring92/mga-r-rest-v2
- https://www.kaggle.com/datasets/conjuring92/mga-r-scatter-matcha
My solution relies on generating a large synthetic dataset for training of models. The dataset is hosted here. The following sections describe the steps to generate the synthetic dataset. You can customize parameters such as number of generated images, output locations and other details using the config files located in the gen/conf directory. Note that, generation code assumes fonts such as Helvetica, Courier New, Times New Roman, Lucida Grande, Arial and Verdana are pre-installed.
Basic vertical bar plots are generated using the following script:
python ./gen/run_gen_vbar.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_vbar.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Vertical bar plots with more customization are generated using the following script:
python ./gen/run_gen_vbar_a0.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_vbar_a0.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Basic horizontal bar plots are generated using the following script:
python ./gen/run_gen_hbar.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_hbar.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Horizontal bar plots with more customization are generated using the following script:
python ./gen/run_gen_hbar_a0.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_hbar_a0.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
The dot plots are generated using the following script:
python ./gen/run_gen_dot.py \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_dot.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
The basic line plots are generated using the following script:
python ./gen/run_gen_line.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_line.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Line plots with more customization are generated using the following script:
python ./gen/run_gen_line_a0.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_line_a0.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Line plots with shared origin edge case are generated using the following script:
python ./gen/run_gen_line_shared.py \
--conf_path ./gen/conf/conf_line_shared.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Line plots with even more diverse styles are generated using the following script:
python ./gen/run_gen_line_a3.py \
--wiki_path ../datasets/processed/deps/sanitized_wiki.json \
--stem_path ../datasets/processed/deps/mga_stem_kws.pickle \
--conf_path ./gen/conf/conf_line_a3.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
The basic scatter plots are generated using the following script:
python ./gen/run_gen_scatter.py \
--conf_path ./gen/conf/conf_scatter.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
Scatter plots with more customization are generated using the following script:
python ./gen/run_gen_scatter_a0.py \
--conf_path ./gen/conf/conf_scatter_a0.yaml \
--texture_dir ../datasets/processed/deps/mga_textures_cc/mga_textures_cc
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