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HAWPv2: Learning Wireframes via Fully-Supervised Learning

The codes of HAWPv2 are placed in the directory of hawp/fsl.

Quickstart & Evaluation

  • Please download the dataset and checkpoints as in readme.md.

  • Run the following command line(s) to evaluate the offical model on the Wireframe dataset and YorkUrban dataset by

    Evaluation on the Wireframe dataset. 
    python -m hawp.fsl.benchmark configs/hawpv2.yaml \
  --ckpt checkpoints/hawpv2-edb9b23f.pth \
  --dataset wireframe
    Evaluation on the YorkUrban dataset. 
    python -m hawp.fsl.benchmark configs/hawpv2.yaml \
  --ckpt checkpoints/hawpv2-edb9b23f.pth \
  --dataset wireframe

Evaluation Results

Dataset sAP-5 sAP-10 sAP-15 command line comment
Wireframe 65.8 69.8 71.4 python -m hawp.fsl.benchmark configs/hawpv2.yaml --ckpt checkpoints/hawpv2-edb9b23f.pth --dataset wireframe --jhm=0.001 jhm = 0.001
Wireframe 65.7 69.8 71.4 python -m hawp.fsl.benchmark configs/hawpv2.yaml --ckpt checkpoints/hawpv2-edb9b23f.pth --dataset wireframe --jhm=0.005 jhm = 0.005
Wireframe 65.7 69.7 71.3 python -m hawp.fsl.benchmark configs/hawpv2.yaml --ckpt checkpoints/hawpv2-edb9b23f.pth --dataset wireframe --jhm=0.008 jhm = 0.008 (default setting)
YorkUrban 29.0 31.4 32.8 python -m hawp.fsl.benchmark configs/hawpv2.yaml --ckpt checkpoints/hawpv2-edb9b23f.pth --dataset york --jhm=0.005 jhm = 0.001
YorkUrban 28.9 31.4 32.7 python -m hawp.fsl.benchmark configs/hawpv2.yaml --ckpt checkpoints/hawpv2-edb9b23f.pth --dataset york --jhm=0.005 jhm = 0.005
YorkUrban 28.8 31.3 32.6 python -m hawp.fsl.benchmark configs/hawpv2.yaml --ckpt checkpoints/hawpv2-edb9b23f.pth --dataset york --jhm=0.005 jhm = 0.008

Training

  • Run the following command line to train the HAWPv2 on the Wireframe dataset.

    python -m hawp.fsl.train configs/hawpv2.yaml --logdir outputs

  • The usage of hawp.fsl.train is as follow:

    HAWPv2 Training

positional arguments:
  config              path to config file

optional arguments:
  -h, --help          show this help message and exit
  --logdir LOGDIR
  --resume RESUME
  --clean
  --seed SEED
  --tf32              toggle on the TF32 of pytorch
  --dtm {True,False}  toggle the deterministic option of CUDNN. This option will affect the replication of experiments