This repository provides the official code for the paper "Constrained Graphic Layout Generation via Latent Optimization", especially the code for:
- LayoutGAN++: generative adversarial networks for layout generation
- CLG-LO: a framework for generating layouts that satisfy constraints
- Layout evaluation: measuring the quantitative metrics of Layout FID, Maximum IoU, Alignment, and Overlap for generated layouts
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Clone this repository
git clone https://github.com/ktrk115/const_layout.git cd const_layout -
Create a new conda environment (Python 3.8)
conda create -n const_layout python=3.8 conda activate const_layout
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Install PyTorch 1.8.1 and PyTorch Geometric 1.7.2. An example of the PyG installation command is shown below.
pip install torch-scatter==2.0.7 -f https://data.pyg.org/whl/torch-1.8.1+cu111.html pip install torch-sparse==0.6.10 -f https://data.pyg.org/whl/torch-1.8.1+cu111.html pip install torch-geometric==1.7.2
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Install the other dependent libraries
pip install -r requirements.txt
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Prepare data (see this instruction)
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Download pre-trained models
./download_model.sh
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Install ImageMagick for visualization
- Ubuntu 18.04, CUDA 11.1
python generate.py pretrained/layoutganpp_rico.pth.tar --out_path output/generated_layouts.pkl --num_save 5
python train.py --dataset rico --batch_size 64 --iteration 200000 --latent_size 4 --lr 1e-05 --G_d_model 256 --G_nhead 4 --G_num_layers 8 --D_d_model 256 --D_nhead 4 --D_num_layers 8
| w/ beautification constraints | w/ relational constraints |
|---|---|
python generate_const.py pretrained/layoutganpp_publaynet.pth.tar --const_type beautify --out_path output/beautify/generated_layouts.pkl --num_save 5
python generate_const.py pretrained/layoutganpp_publaynet.pth.tar --const_type relation --out_path output/relation/generated_layouts.pkl --num_save 5
python eval.py rico output/generated_layouts.pkl
A pickle file should be a list of layouts, where each layout is a tuple of bounding boxes and labels. The bounding box is represented by [x, y, width, height] in normalized coordinates, and the label is represented by an index. An example is shown below.
In [x]: layouts
Out[x]:
[(array([[0.47403812, 0.11276676, 0.6250037 , 0.02210438],
[0.49971417, 0.8550553 , 0.81388366, 0.03492427],
[0.49919674, 0.47857162, 0.81024694, 0.7070079 ]], dtype=float32),
array([0, 0, 3]),
...
If this repository helps your research, please consider citing our paper.
@inproceedings{Kikuchi2021, title = {Constrained Graphic Layout Generation via Latent Optimization}, author = {Kotaro Kikuchi and Edgar Simo-Serra and Mayu Otani and Kota Yamaguchi}, booktitle = {ACM International Conference on Multimedia}, series = {MM '21}, year = {2021}, pages = {88--96}, doi = {10.1145/3474085.3475497} }
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