rammyram / TF2DeepFloorplan

A Tensorflow2 implementation of Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention paper. Also supports tensorboard, quantization, tflite, docker, github actions and google colab.

TF2DeepFloorplan

This repo contains a basic procedure to train and deploy the DNN model suggested by the paper 'Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'. It rewrites the original codes from zlzeng/DeepFloorplan into newer versions of Tensorflow and Python.
Network Architectures from the paper,

Requirements

Install the packages stated in requirements.txt, including matplotlib,numpy,opencv-python,pdbpp, tensorflow-gpu and tensorboard.
The code has been tested under the environment of Python 3.7.4 with tensorflow-gpu==2.3.0, cudnn==7.6.5 and cuda10.1_0. Used Nvidia RTX2080-Ti eGPU, 60 epochs take approximately 1 hour to complete.

How to run?

1. Install packages via pip and requirements.txt.

pip install -r requirements.txt

2. According to the original repo, please download r3d dataset and transform it to tfrecords r3d.tfrecords.
3. Run the train.py file to initiate the training, model weight is stored as log/store/G,

python train.py [--batchsize 2][--lr 1e-4][--epochs 1000]
[--logdir 'log/store'][--modeldir 'model/store']
[--saveTensorInterval 10][--saveModelInterval 20]

• for example,

python train.py --batchsize=8 --lr=1e-4 --epochs=60 
--logdir=log/store --modeldir=model/store

4. Run Tensorboard to view the progress of loss and images via,

tensorboard --logdir=log/store

5. Convert model to tflite via convert2tflite.py.

python convert2tflite.py [--modeldir model/store]
[--tflitedir model/store/model.tflite]
[--quantize]

6. Download and unzip model from google drive,

gdown https://drive.google.com/uc?id=1czUSFvk6Z49H-zRikTc67g2HUUz4imON # log files 112.5mb
unzip log.zip 
gdown https://drive.google.com/uc?id=1tuqUPbiZnuubPFHMQqCo1_kFNKq4hU8i # pb files 107.3mb
unzip model.zip
gdown https://drive.google.com/uc?id=1B-Fw-zgufEqiLm00ec2WCMUo5E6RY2eO # tfilte file 37.1mb
unzip tflite.zip

7. Deploy the model via deploy.py, please be aware that load method parameter should match with weight input.

python deploy.py [--image 'path/to/image']
[--postprocess][--colorize][--save 'path/to/output_image']
[--loadmethod 'log'/'pb'/'tflite']
[--weight 'log/store/G'/'model/store'/'model/store/model.tflite']

• for example,

python deploy.py --image floorplan.jpg --weight log/store/G 
--postprocess --colorize --save output.jpg --loadmethod log

Docker for API

1. Build and run docker container.

docker build -t tf_docker -f Dockerfile .
docker run -d -p 1111:1111 tf_docker:latest 
docker run --gpus all -d -p 1111:1111 tf_docker:latest 

2. Call the api for output.

curl -H "Content-Type: application/json" --request POST  \
  -d '{"uri":"https://cdn.cnn.com/cnnnext/dam/assets/200212132008-04-london-rental-market-intl-exlarge-169.jpg","colorize":1,"postprocess":0}'  \
  http://0.0.0.0:1111/process --output out.jpg 
curl --request POST -F "file=@/home/yui/Pictures/4plan/tmp.jpeg;type=image/jpeg" \
  -F "postprocess=0" -F "colorize=0"  http://0.0.0.0:1111/process --output out.jpg

Google Colab

1. Click on and authorize access.
2. Run the first code cell for installation.
3. Go to Runtime Tab, click on Restart runtime. This ensures the packages installed are enabled.
4. Run the rest of the notebook.

Results

• From train.py and tensorboard.

Compare Ground Truth (top) against Outputs (bottom)	Total Loss
Boundary Loss	Room Loss

• From deploy.py and utils/legend.py.

Input	Legend	Output
--colorize	--postprocess	--colorize --postprocess