We recommend users to use conda to install the running environment. The following dependencies are required:
CUDA=11.1
Python=3.7.7
pytorch=1.9.0
torchvision=0.10.0
sklearn=1.0.1
pillow=8.3.1
opencv-python
matplotlib
scipy
tqdm
gdown
svgpathtools
Our code should compatible with pytorch>=1.5.0
The Input Embedding network is based on HRNet-W48-C, the pretrained model on ImageNet can be download from official cloud drive.
cd CADTransformer
mkdir pretrained_models
Put the downloaded pretrained HRNet to CADTransformer/pretrained_models/
We provide several samples of the converted data, users can run our code without downloading from official FloorPlanCAD dataset from its website. In order to train the model on entire FloorPlanCAD dataset, users need first download data from official cloud drive. Then unzip and re-arrange files according to the following commands to form the this directory structure:
python preprocess/download_data.py --data_save_dir /ssd1/zhiwen/datasets/svg_raw
python preprocess/svg2png.py --train_00 /ssd1/zhiwen/datasets/svg_raw/train-00 --train_01 /ssd1/zhiwen/datasets/svg_raw/train-01 --test_00 /ssd1/zhiwen/datasets/svg_raw/test-00 --svg_dir /ssd1/zhiwen/datasets/svg_processed/svg --png_dir /ssd1/zhiwen/datasets/svg_processed/png --scale 7 --cvt_color
python preprocess/preprocess_svg.py -i /ssd1/zhiwen/datasets/svg_processed/svg/train -o /ssd1/zhiwen/datasets/svg_processed/npy/train --thread_num 48
python preprocess/preprocess_svg.py -i /ssd1/zhiwen/datasets/svg_processed/svg/test -o /ssd1/zhiwen/datasets/svg_processed/npy/test --thread_num 48
python preprocess/preprocess_svg.py -i /ssd1/zhiwen/datasets/svg_processed/svg/val -o /ssd1/zhiwen/datasets/svg_processed/npy/val --thread_num 48
mkdir data ln -s /ssd1/zhiwen/datasets/svg_processed ./data/floorplancad_v2
├── data
├──├── FloorPlanCAD
├──├──├── npy(converted using script)
│ │ │ └── test
│ │ │ └── train
│ │ │ └── val
├──├──├── png(converted using script)
│ │ │ └── test
│ │ │ └── train
│ │ │ └── val
├──├──├── svg(download from (https://floorplancad.github.io/))
│ │ │ └── test
│ │ │ └── train
│ │ │ └── val
After installing the required libraries, users can directly train CADTransformer using the provided data samples
CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 train_cad_ddp.py --data_root /PATH/TO/PROJECT_DIR/data/FloorPlanCAD --pretrained_model /PATH/TO/PROJECT_DIR/pretrained_models/hrnetv2_w48_imagenet_pretrained.pth
One can speed up the training process by using multiple GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train_cad_ddp.py --data_root /PATH/TO/PROJECT_DIR/data/FloorPlanCAD --pretrained_model /PATH/TO/PROJECT_DIR/pretrained_models/hrnetv2_w48_imagenet_pretrained.pth
Users can directly do testing/validation of the CADTransformer using the provided data samples
CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 train_cad_ddp.py --data_root /PATH/TO/PROJECT_DIR/data/FloorPlanCAD --pretrained_model /PATH/TO/PROJECT_DIR/pretrained_models/hrnetv2_w48_imagenet_pretrained.pth --test_only
CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 train_cad_ddp.py --data_root /PATH/TO/PROJECT_DIR/data/FloorPlanCAD --pretrained_model /PATH/TO/PROJECT_DIR/pretrained_models/hrnetv2_w48_imagenet_pretrained.pth --val_only
Users can obtain the Panoptic Quality metric via the following command:
python scripts/evaluate_pq.py --raw_pred_dir /PATH/TO/SAVE_DIR/IN/PREVIOUS/STEP
--svg_pred_dir /PATH/TO/PROJECT_DIR/FloorPlanCAD/svg_pred --svg_gt_dir /PATH/TO/PROJECT_DIR/FloorPlanCAD/svg_gt --thread_num 6
[A]: Reduce the max primitive number via args.max_prim. I set it as 12000 on a 24GB GPU.
Thanks to Ross Wightman, qq456cvb, Ke Sun for opening source of their excellent works pytorch-image-models, Point-Transformers, HRNet.
If you find our code implementation helpful for your own resarch or work, please cite our paper.
@inproceedings{fan2022cadtransformer,
title={CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings},
author={Fan, Zhiwen and Chen, Tianlong and Wang, Peihao and Wang, Zhangyang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10986--10996},
year={2022}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent