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