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.
Depends on different applications, the following installation methods can
| OS | Hardware | Application | Command |
|---|---|---|---|
| Ubuntu | CPU | Model Development | pip install -e .[tfcpu,dev,testing,linting] |
| Ubuntu | GPU | Model Development | pip install -e .[tfgpu,dev,testing,linting] |
- Install packages.
# Option 1
python -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools wheel
# Option 2 (Preferred)
conda create -n venv python=3.8 cudatoolkit=10.1 cudnn=7.6.5
conda activate venv
# common install
pip install -e .[tfgpu,api,dev,testing,linting]
- Create a Structured3D dataset and transform it to tfrecords
tf2deep.tfrecords. - Run the
train_furn.pyfile to initiate the training, model checkpoint is stored aslog/store/Gand weight is inmodel/store,
python -m dfp.train_furn [--batchsize 1][--lr 1e-4][--epochs 100]
[--logdir 'log/store'][--modeldir 'model/store']
[--save-tensor-interval 10][--save-model-interval 20]
[--tfmodel 'subclass'/'func'][--feature-channels 256 128 64 32]
[--backbone 'vgg16'/'mobilenetv1'/'mobilenetv2'/'resnet50']
[--feature-names block1_pool block2_pool block3_pool block4_pool block5_pool]
- for example,
python -m dfp.train_furn --batchsize=1 --lr=5e-4 --epochs=100
--logdir=log/store --modeldir=model/store
- Run Tensorboard to view the progress of loss and images via,
tensorboard --logdir=log/store