DET is an end-to-end tool for extracting Key-Value pairs from a variety of documents, built entirely on PyTorch and served using TorchServe.
Try it live on the web-app! 👋
The Document Extraction tool is composed of two main components, namely,
1. OCR (Optical Character Recognition) and
2. Document NER (Named Entity Recognition)
The OCR component comprises of a Detection and Recognition module which work sequentially to produce results which is then consumed by the NER part for training/prediction.
For the OCR part, we have deployed a torchserve model server on GCP using Vertex AI service. Using torchserve, we deploy a complex workflow in the form of a DAG, comprising of pre_processing, detection and recognition models.
For the NER part, we created a training module using transformers library which requires the text and bounding-box results from OCR output to train/predict documents.
The architectural flow of the two modules is shown
- Optical Character Recognition (OCR):
- Detection:
- Recognition:
- Named Entity Recognition (NER):
- Receipt Dataset (SROIE):
- Forms Dataset (FUNSD):
deploy # INSTRUCTIONS/SCRIPTS for deploying model(s) on GPU/CPU
|---GPU # For Serving models on GPU
|------jit-models # contains jitted models created after tracing the model using torch.jit.trace
|------------craft_ts.pt # DOWNLOAD the torchscript files from here: https://drive.google.com/drive/folders/1NBSZIZzSzIVOUqnxu0PHgmy-_Tvvp2hY?usp=sharing
|------------crnn_ts.pt
|------------sroie_ts.pt
|------------funsd_ts.pt
|------model-archive # stores models along with configuration in .mar format as required by "torchserve"
|------------model_store # GENERATE standalone .mar files from torch-model-archiver command given below
|------------------craft.mar
|------------------crnn.mar
|------------------sroie.mar
|------------------funsd.mar
|------------wf_store # GENERATE workflow .mar files from torch-workflow-archiver command given below
|------------------ocr.mar
|------------------ner_sroie.mar
|------------------ner_funsd.mar
|------config.properties # config.properties for storing serving related configurations.
| For details, refer this: https://github.com/pytorch/serve/blob/master/docs/configuration.md
|------detection_handler.py # handler for text detection pipeline
|------rec_handler.py # handler for text recognition pipeline
|------workfow_handler.py # handler for configuring end2end ocr serving pipeline
|------workflow_ocr.yaml # define the pipeline here, also specify batch-size, num workers etc.
|------workflow_ner_sroie.yaml
|------workflow_ner_funsd.yaml
|------Dockerfile # template for creating GPU docker image for UCR
|------.dockerignore # files to ignore in docker image
|------cloud_deploy.ipynb # scripts to deploy the file on GCP Vertex ai and run predictions.
|------sample_b64.json # sample file to send request on inference api
|------index_sroie.json # contains mapping of indexes to human-readable labels for SROIE dataset
|------index_funsd.json # contains mapping of indexes to human-readable labels for FUNSD dataset
|---CPU # Same as above except it's CPU (currently INCOMPLETE)
|------jit-models
|------------craft_ts.pt
|------------crnn_ts.pt
|------model-archive
|------------model_store
|------------------craft.mar
|------------------crnn.mar
|------------wf_store
|------------------ocr.mar
|------config.properties
|
|------detection_handler.py
|------rec_handler.py
|------workfow_handler.py
|------workflow.yaml
|------Dockerfile
train # NOTEBOOKS for training models on GPU/CPU. More training scripts COMING SOON!
|---NER.ipynb # Jupyter Notebook to train,test Document NER models and convert it to torchscript format.
Try it live here! 👋
Install Docker and NVIDIA Container Toolkit: See this link for help!
Download and start docker:
docker run -d --gpus all -p 7080:7080 -p 7081:7081 -p 7082:7082 abhigyanr/det-gpu:latest
For testing API, follow these steps! Note: This method requires NVIDIA GPU and driver to be present!
Install docker and nvidia container toolkit: See this link for help!
Clone this repository and change directory:
git clone https://github.com/RamanHacks/pytorch-hackathon.git
cd pytorch-hackathon && cd deploy
cd GPU # OR (cd CPU)
Build Docker Image
docker build -f Dockerfile -t det .
Run Docker container
docker run -d --rm --name det-cpu -p 7080:7080 -p 7081:7081 -p 7082:7082 det # (For CPU)
docker run -d --rm --name det-gpu --gpus all -p 7080:7080 -p 7081:7081 -p 7082:7082 det # (For GPU, use --gpus '"device=0,1"' to specify device)
Optional: Check Status
docker logs $(docker ps -l -q) # to check if the docker container is running fine
curl localhost:7080/ping # to check if the network is accessible from localhost, should return Healthy
Register Models
curl -X POST "localhost:7081/workflows?url=ocr.war"
curl -X POST "localhost:7081/workflows?url=ner_sroie.war"
curl -X POST "localhost:7081/workflows?url=ner_funsd.war"
Optional: Stop and Remove Container
docker stop $(docker ps -l -q)
docker rm $(docker ps -l -q)
For testing API, follow these steps!
Install torch from official link: PyTorch Official
Install torchserve from official repo: TorchServe Official
Clone this repository and install dependencies:
git clone https://github.com/RamanHacks/pytorch-hackathon.git
cd pytorch-hackathon && cd deploy
pip install -r requirements.txt
cd GPU # OR (cd CPU)
Download pretrained torchscript models from Google Drive and move it inside jit-models folder.
Generate .mar files:
# create model archives
torch-model-archiver -f --model-name craft --version 1.0 --serialized-file jit-models/craft_ts.pt --handler det_handler.py --export-path model-archive/model_store/
torch-model-archiver -f --model-name crnn --version 1.0 --serialized-file jit-models/crnn_ts.pt --handler rec_handler.py --export-path model-archive/model_store/
cp index_sroie.json index.json
torch-model-archiver -f --model-name sroie --version 1.0 --serialized-file jit-models/sroie_ts.pt --handler ext_handler.py --export-path model-archive/model_store/ --extra-files index.json
cp index_funsd.json index.json
torch-model-archiver -f --model-name funsd --version 1.0 --serialized-file jit-models/funsd_ts.pt --handler ext_handler.py --export-path model-archive/model_store/ --extra-files index.json
rm index.json
# create workflow archives
torch-workflow-archiver -f --workflow-name ocr --spec-file workflow_ocr.yaml --handler workflow_handler.py --export-path model-archive/wf_store/
torch-workflow-archiver -f --workflow-name ner_sroie --spec-file workflow_ner_sroie.yaml --handler workflow_handler.py --export-path model-archive/wf_store/
torch-workflow-archiver -f --workflow-name ner_funsd --spec-file workflow_ner_funsd.yaml --handler workflow_handler.py --export-path model-archive/wf_store/
Start Model Server
torchserve --start --model-store model-archive/model_store/ --workflow-store model-archive/wf_store/ --ncs --ts-config config.properties
Register Models
curl -X POST "localhost:7081/workflows?url=ocr.war"
curl -X POST "localhost:7081/workflows?url=ner_sroie.war"
curl -X POST "localhost:7081/workflows?url=ner_funsd.war"
Optional: Stop TorchServe
torchserve --stop
For testing API, follow these steps!
Request format: Create a sample
jsonfile containing base64 values of image
{
'b64': '<base64 value of an image>'
}Response format of OCR(only), i.e. when hitting "/wfpredict/ocr":
[
{
'bbox': [[<top-left>],[<top-right>],[<bottom-left>],[<bottom-right>]],
'ocr': [<value>, <confidence>],
}
]Response format of OCR+NER, i.e. when hitting "/wfpredict/ner_sroie" or "/wfpredict/ner_funsd":
[
{
'bbox': [<top-left-x>,<top-left-y>,<bottom-right-x>,<bottom-right-y>],
'ocr': <value>,
'key': <value>,
}
]Sample CURL Request
curl -X POST -H "Content-Type: application/json; charset=utf-8" -d @sample_b64.json localhost:7080/wfpredict/ner_sroie
Python function to convert an image into base64, send request and return predictions
import base64
import requests
def sample_request(image_file_path)
def convert_b64(image_file):
"""Open image and convert it to Base64"""
with open(image_file, "rb") as input_file:
jpeg_bytes = base64.b64encode(input_file.read()).decode("utf-8")
return jpeg_bytes
req = {"data": convert_b64(image_file_path)}
res = requests.post("http://localhost:7080/wfpredict/ner_sroie", json=req)
return res.json()Jump to NER.ipynb for details on training and testing Document NER models!
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