Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization

Related tags

Deep Learning YOLaT-VectorGraphicsRecognition
Overview

YOLaT-VectorGraphicsRecognition

arXiv

This repository is the official PyTorch implementation of our NeurIPS-2021 paper: Recognizing Vector Graphics without Rasterization.

Environments

conda create -n your_env_name python=3.8
conda activate your_env_name
sh deepgcn_env_install.sh

YOLaT

1. Data Preparation

Floorplans

a) Download and unzip the Floorplans dataset to the dataset folder: data/FloorPlansGraph5_iter

b) Run the following scripts to prepare the dataset for training/inference.

python utils/svg_utils/build_graph_bbox.py

Diagrams

a) Download and unzip the Diagrams dataset to the dataset folder: data/diagrams

b) Run the following scripts to prepare the dataset for training/inference.

python utils/svg_utils/build_graph_bbox_diagram.py

2. Training & Inference

Floorplans

cd cad_recognition
CUDA_VISIBLE_DEVICES=0 python -u train.py --batch_size 4 --data_dir data/FloorPlansGraph5_iter --phase train --lr 2.5e-4 --lr_adjust_freq 9999999999999999999999999999999999999 --in_channels 5 --n_blocks 2 --n_blocks_out 2 --arch centernet3cc_rpn_gp_iter2  --graph bezier_cc_bb_iter --data_aug true  --weight_decay 1e-5 --postname run182_2 --dropout 0.0 --do_mixup 0 --bbox_sampling_step 10

Diagrams

cd cad_recognition
CUDA_VISIBLE_DEVICES=0 python -u train.py --batch_size 4 --data_dir data/diagrams --phase train --lr 2.5e-4 --lr_adjust_freq 9999999999999999999999999999999999999 --in_channels 5 --n_blocks 2 --n_blocks_out 2 --arch centernet3cc_rpn_gp_iter2  --graph bezier_cc_bb_iter --data_aug true  --weight_decay 1e-5 --postname run182_2 --dropout 0.0 --do_mixup 0 --bbox_sampling_step 5

Citation

  @inproceedings{jiang2021recognizing,
  title={{Recognizing Vector Graphics without Rasterization}},
  author={Jiang, Xinyang and Liu, Lu and Shan, Caihua and Shen, Yifei and Dong, Xuanyi and Li, Dongsheng},
  booktitle={Proceedings of Advances in Neural Information Processing Systems (NIPS)},
  volume={34},
  number={},
  pages={},
  year={2021}}
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Comments
  • About test.py

    About test.py

    Dr.Jiang, Sorry to bother you. I run the command "CUDA_VISIBLE_DEVICES=0 python -u cad_recognition/test.py --data_dir data/FloorPlansGraph5_iter --pretrained_model log/run182_2_best.pth" with codes about "opt.arch" and "opt.graph" being commented out. BUT before and then, I still got the errors: "size mismatch for cls_net.fusion_block.0.weight: copying a param with shape torch.Size([1024, 128]) from checkpoint, the shape in current model is torch.Size([1024, 448]). size mismatch for cls_net.fusion_block_super.0.weight: copying a param with shape torch.Size([1024, 128]) from checkpoint, the shape in current model is torch.Size([1024, 448]). size mismatch for prediction_cls.0.0.weight: copying a param with shape torch.Size([512, 2304]) from checkpoint, the shape in current model is torch.Size([512, 2944])." It really confusing since the model was saved based on "def save_checkpoint()" while it did not match during loading the model. Would you like to resolve this issue? Thanks a lot and looking forward to your response soon.

    Best regards, VivianBB.

    opened by BioDPJ 2
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