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Pix2Struct

This repository contains code for Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding.

We release pretrained checkpoints for the Base and Large models and code for finetuning them on the nine downstream tasks discussed in the paper. We are unable to release the pretraining data, but they can be replicated using the publicly available URLs released in the C4 dataset.

Getting Started

Clone the github repository, install the pix2struct package, and run the tests to ensure that all dependencies were successfully installed.

git clone https://github.com/google-research/pix2struct.git
cd pix2struct
conda create -n pix2struct python=3.9
conda activate pix2struct
pip install -e ."[dev]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
pytest

You may first need to install Java (sudo apt install default-jre) and conda if not already installed.

We will be using Google Cloud Storage (GCS) for data and model storage. For the remaining documentation we will assume that the path to your own bucket and directory is in the PIX2STRUCT_DIR environment variable:

export PIX2STRUCT_DIR="gs://<your_bucket>/<path_to_pix2struct_dir>"

The code for running experiments assumes this environment variable when looking for the preprocessed data.

Data Preprocessing

Our data preprocessing scripts are run with Dataflow by default using the Apache Beam library. They can also be run locally by turning off flags appearing after --.

For the remaining documentation we will assume that GCP project information is in the following environment variables:

export GCP_PROJECT=<your_project_id>
export GCP_REGION=<your_region>

Below are the commands required to preprocess each dataset. The results will be written to $PIX2STRUCT_DIR/data/<task_name>/preprocessed/, which is the file structure assumed in tasks.py.

TextCaps

mkdir -p data/textcaps
cd data/textcaps
curl -O https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_train.json
curl -O https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_val.json
curl -O https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_test.json
curl -O https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
curl -O https://dl.fbaipublicfiles.com/textvqa/images/test_images.zip
unzip train_val_images.zip
rm train_val_images.zip
unzip test_images.zip
rm test_images.zip
cd ..
gsutil -m cp -r textcaps_data $PIX2STRUCT_DIR/data/textcaps
python -m pix2struct.preprocessing.convert_textcaps \
  --textcaps_dir=$PIX2STRUCT_DIR/data/textcaps \
  --output_dir=$PIX2STRUCT_DIR/data/textcaps/processed \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

ChartQA

mkdir -p data/chartqa
cd data/chartqa
git clone https://github.com/vis-nlp/ChartQA.git
cp -r ChartQA/ChartQA\ Dataset/* ./
rm -rf ChartQA
cd ..
gsutil -m cp -r chartqa $PIX2STRUCT_DIR/data/chartqa
python -m pix2struct.preprocessing.convert_chartqa \
  --data_dir=$PIX2STRUCT_DIR/data/chartqa \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

RICO Images

Screen2Words, RefExp, and Widget Captioning all require images from the RICO dataset. If you'd like to use any of these datasets, please process RICO images before proceeding.

cd data
wget https://storage.googleapis.com/crowdstf-rico-uiuc-4540/rico_dataset_v0.1/unique_uis.tar.gz
tar xvfz unique_uis.tar.gz
rm unique_uis.tar.gz
gsutil -m cp -r combined $PIX2STRUCT_DIR/data/rico_images

Widget Captioning

If you haven't already setup RICO, please do so before you proceed.

mkdir -p data/widget_captioning
cd data/widget_captioning
git clone https://github.com/google-research-datasets/widget-caption.git
cp widget-caption/widget_captions.csv ./
cp widget-caption/split/*.txt ./
mv dev.txt val.txt
rm -rf widget-caption
cd ..
gsutil -m cp -r widget_captioning $PIX2STRUCT_DIR/data/widget_captioning
python -m pix2struct.preprocessing.convert_widget_captioning \
  --data_dir=$PIX2STRUCT_DIR/data/widget_captioning \
  --image_dir=$PIX2STRUCT_DIR/data/rico_images \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

Screen2Words

If you haven't already setup RICO, please do so before you proceed.

cd data
git clone https://github.com/google-research-datasets/screen2words.git
gsutil -m cp -r screen2words $PIX2STRUCT_DIR/data/screen2words
python -m pix2struct.preprocessing.convert_screen2words \
  --screen2words_dir=$PIX2STRUCT_DIR/data/screen2words \
  --rico_dir=$PIX2STRUCT_DIR/data/rico_images \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

RefExp

If you haven't already setup RICO, please do so before you proceed.

mkdir -p data/refexp
cd data/refexp
wget https://github.com/google-research-datasets/uibert/raw/main/ref_exp/train.tfrecord
wget https://github.com/google-research-datasets/uibert/raw/main/ref_exp/dev.tfrecord
wget https://github.com/google-research-datasets/uibert/raw/main/ref_exp/test.tfrecord
mv dev.tfrecord val.tfrecord
cd ..
gsutil -m cp -r refexp $PIX2STRUCT_DIR/data/refexp
python -m pix2struct.preprocessing.convert_refexp \
  --data_dir=$PIX2STRUCT_DIR/data/refexp \
  --image_dir=$PIX2STRUCT_DIR/data/rico_images \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

DocVQA

mkdir -p data/docvqa
cd data/docvqa

Download DocVQA (Single Document Visual Question Answering) from the official source (requires registration). The following steps assume that the train/val/test.tar.gz files are in data/docvqa.

tar xvf train.tar.gz
tar xvf val.tar.gz
tar xvf test.tar.gz
rm -r *.tar.gz */ocr_results

cd ..
gsutil -m cp -r docvqa $PIX2STRUCT_DIR/data/docvqa
python -m pix2struct.preprocessing.convert_docvqa \
  --data_dir=$PIX2STRUCT_DIR/data/docvqa \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

InfographicVQA

mkdir -p data/infographicvqa
cd data/infographicvqa

Download InfographicVQA Task 1 from this website (requires registration). The following steps assume that the train/val/test.json and the zip files are in data/infographicvqa.

for split in train val test
do
  unzip infographicVQA_${split}_v1.0_images.zip
  mv infographicVQA_${split}_v1.0_images $split
  mv infographicVQA_${split}_v1.0.json $split/${split}_v1.0.json
done
rm *.zip

cd ..
gsutil -m cp -r infographicvqa $PIX2STRUCT_DIR/data/infographicvqa
python -m pix2struct.preprocessing.convert_docvqa \
  --data_dir=$PIX2STRUCT_DIR/data/infographicvqa \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

OCR-VQA

mkdir -p data/ocrvqa
cd data/ocrvqa

Follow instructions on the OCR-VQA website to download the data into data/ocrvqa (requires crawling). The following steps assume that data/ocrvqa contains a directory called images and a file called dataset.json.

cd ..
gsutil -m cp -r ocrvqa $PIX2STRUCT_DIR/data/ocrvqa
python -m pix2struct.preprocessing.convert_ocrvqa \
  --data_dir=$PIX2STRUCT_DIR/data/ocrvqa \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

AI2D

mkdir -p data/
cd data/
wget https://ai2-public-datasets.s3.amazonaws.com/diagrams/ai2d-all.zip
unzip ai2d-all.zip
rm ai2d-all.zip
gsutil -m cp -r ai2d $PIX2STRUCT_DIR/data/ai2d
python -m pix2struct.preprocessing.convert_ai2d \
  --data_dir=$PIX2STRUCT_DIR/data/ai2d \
  --test_ids_path=gs://pix2struct-data/ai2d_test_ids.csv \
  -- \
  --runner=DataflowRunner \
  --save_main_session \
  --project=$GCP_PROJECT \
  --region=$GCP_REGION \
  --temp_location=$PIX2STRUCT_DIR/data/temp \
  --staging_location=$PIX2STRUCT_DIR/data/staging \
  --setup_file=./setup.py

Running experiments

The main experiments are implemented as a light wrapper around the T5X library. For brevity, we illustrate an example workflow of finetuning the pretrained base Pix2Struct model on the Screen2Words dataset. To scale up to larger setups, please see to the T5X documentation.

Setting up the TPU

Following official instructions for running JAX on a Cloud TPU VM, which allows you to directly ssh into the TPU host.

In this example, we are using a v3-8 TPU:

TPU_TYPE=v3-8
TPU_NAME=pix2struct-$TPU_TYPE
TPU_ZONE=europe-west4-a
gcloud compute tpus tpu-vm create $TPU_NAME \
  --zone=$TPU_ZONE \
  --accelerator-type=$TPU_TYPE \
  --version=tpu-vm-base
gcloud compute tpus tpu-vm ssh $TPU_NAME --zone=$TPU_ZONE

Once you have sshed into the TPU host, follow the "Getting Started" instructions to install the pix2struct package.

Training

The following command will initiate the training loop, which consists of train steps interleaved with evaluations on the validation set.

python -m t5x.train \
  --gin_search_paths="pix2struct/configs" \
  --gin_file="models/pix2struct.gin" \
  --gin_file="runs/train.gin" \
  --gin_file="sizes/base.gin" \
  --gin_file="optimizers/adafactor.gin" \
  --gin_file="schedules/screen2words.gin" \
  --gin_file="init/pix2struct_base_init.gin" \
  --gin.MIXTURE_OR_TASK_NAME="'screen2words'" \
  --gin.MODEL_DIR="'$PIX2STRUCT_DIR/experiments/screen2words_base'" \
  --gin.TASK_FEATURE_LENGTHS="{'inputs': 4096, 'targets': 128}" \
  --gin.BATCH_SIZE=32

Evaluation

The following command evaluates the model on the test set. You will need to replace the checkpoint path with the one that was actually selected based on the validation performance.

python -m t5x.eval \
  --gin_search_paths="pix2struct/configs" \
  --gin_file="models/pix2struct.gin" \
  --gin_file="runs/eval.gin" \
  --gin_file="sizes/base.gin" \
  --gin.MIXTURE_OR_TASK_NAME="'screen2words'" \
  --gin.CHECKPOINT_PATH="'$PIX2STRUCT_DIR/experiments/screen2words_base/checkpoint_286600'" \
  --gin.EVAL_OUTPUT_DIR="'$PIX2STRUCT_DIR/experiments/test_exp/test_eval'" \
  --gin.EVAL_SPLIT="'test'" \
  --gin.TASK_FEATURE_LENGTHS="{'inputs': 4096, 'targets': 128}" \
  --gin.BATCH_SIZE=32

Finetuned Checkpoints

In addition to the pretrained checkpoints released and specified in the configs/init directory. We also release checkpoints for the finetuned models on all tasks below.

These checkpoints are compatible with the eval command documented above and the two ways of performing inference mentioned below. Please ensure that the config file under configs/sizes is set to be consistent with the checkpoint.

Inference

We provide two ways of performing inference. For testing and demoing purposes, these may be run on CPU. In that case, please set the JAX_PLATFORMS environment variable to cpu.

Command-line example

We provide a minimal script for performing inference on a single example. This path has only been tested at extremely small scale and is not meant for larger-scale inference. For large-scale inference, we recommend setting a custom task with placeholder labels and running the evaluation script (t5x.eval) as documented above.

In the following example, we show the command for predicting the caption of an image using a base-sized checkpoint finetuned on the TextCaps task. For a task that also accepts textual prompts such as questions in VQA, you can also supply the question via the text flag (in addition to specifying the image with the image flag).

python -m pix2struct.example_inference \
  --gin_search_paths="pix2struct/configs" \
  --gin_file=models/pix2struct.gin \
  --gin_file=runs/inference.gin \
  --gin_file=sizes/base.gin \
  --gin.MIXTURE_OR_TASK_NAME="'placeholder_pix2struct'" \
  --gin.TASK_FEATURE_LENGTHS="{'inputs': 2048, 'targets': 128}" \
  --gin.BATCH_SIZE=1 \
  --gin.CHECKPOINT_PATH="'gs://pix2struct-data/textcaps_base/checkpoint_280400'" \
  --image=$HOME/test_image.jpg

Web Demo

For a more user-friendly demo, we also provide a web-based alternative of inference script above. While running this command, the web demo can be accessed at localhost:8080 (or any port specified via the port flag), assuming you are running the demo locally. You can then upload your custom image and optional prompt instead of specifying it via the command line.

python -m pix2struct.demo \
  --gin_search_paths="pix2struct/configs" \
  --gin_file=models/pix2struct.gin \
  --gin_file=runs/inference.gin \
  --gin_file=sizes/base.gin \
  --gin.MIXTURE_OR_TASK_NAME="'placeholder_pix2struct'" \
  --gin.TASK_FEATURE_LENGTHS="{'inputs': 2048, 'targets': 128}" \
  --gin.BATCH_SIZE=1 \
  --gin.CHECKPOINT_PATH="'gs://pix2struct-data/textcaps_base/checkpoint_280400'"

Clean up

When you are done with your TPU VM, remember to delete the instance:

gcloud compute tpus tpu-vm delete $TPU_NAME --zone=$TPU_ZONE

Note

This is not an officially supported Google product.