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Training Recipes of HAWPv3

HAWPv3 consists of multiple training phases including the synthetic training phase and the real data training.

Step 0: Synthetic learning

 python -m hawp.ssl.train \
    --datacfg hawp/ssl/config/synthetic_dataset.yaml \
    --modelcfg hawp/ssl/config/hawpv3.yaml \
    --base-lr 0.0004 \
    --epochs 10  \
    --batch-size 6 \
    --name hawpv3-round0

Step 1: Homographic Adaptation for Pseudo Wireframe Generation

If you prefer single-image mode to minimize the usage of GPU memory footprint, please use the following command to obtain pseudo labels

python -m hawp.ssl.homoadp --metarch HAWP-heatmap \
    --datacfg hawp/ssl/config/export/wireframe-10iters.yaml \
    --workdir exp-ssl/hawpv3-round0 \
    --epoch 10 \
    --modelcfg exp-ssl/hawpv3-round0/model.yaml \
    --min_score 0.75

For the batch mode, please use the following command

python -m hawp.ssl.homoadp-bm --metarch HAWP-heatmap \
    --datacfg hawp/ssl/config/exports/wireframe-10iters.yaml \
    --workdir exp-ssl/hawpv3-round0 \
    --epoch 10 \
    --modelcfg exp-ssl/hawpv3-round0/model.yaml \
    --min-score 0.75   --batch-size=16

On my machine (NVIDIA A6000), the batch size of 16 will take 40G GPU memory in 43 minutes to generate the wireframe labels for 20k images of the training images in the Wireframe dataset (Huang et al., CVPR 2018)

Remarks

  1. After the homographic adaptation step finished, three files will be generated and stored at the directory of data-ssl/{name}, where {name} is the name for the last round of training. For example, if we train HAWPv3 with the name of hawpv3-round0, the exported data will be saved at data-ssl/hawpv3-round0.

  2. For each generated wireframe and its auxiluary files, their names are started with {hash}-model-{epoch:05d}.

  3. In sum, the generated datacfg YAML file is located at data-ssl/{name}/{hash}-model-{epoch:05d}.yaml.

Step 2: Learning from Real-World images

  • Once we have the pseudo wireframe labels, we can train HAWPv3 on the real-world images. An example usage is in the below command line:

    python -m hawp.ssl.train  --datacfg data-ssl/export_datasets/{name}/{hash}-model-00010.yaml --modelcfg hawp/ssl/config/hawpv3.yaml --base-lr 0.0004 --epochs 30  --name hawpv3-round1 --batch-size 6

  • Then, we can run the homographic adaptation with new model checkpoints trained on real-world images, and then train/fine-tune a new model to further improve the repeatibility.