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Basic Usage

Training

Please go throught the scripts/train.py file and modify some training configurations according to items marked by TODO, e.g. disable DDP training, handle cluster/slurm-related settings.

See model for details about the baselines supported in this codebase.

To train a model, simply run:

python scripts/train.py --cfg_file $CFG --other_args ...

For example, to train the Global baseline model on PartNet chair, replace $CFG with configs/global/global-32x1-cosine_200e-partnet_chair.py. Other optional arguments include:

  • --category: train the model only on a subset of data, e.g. Chair, Table, Lamp on PartNet
  • --gpus: setting training GPUs, note that by default we are using DDP supported by PyTorch-Lightning
  • --weight: loading pre-trained weights
  • --fp16: FP16 mixed precision training
  • --cudnn: setting cudnn.benchmark = True
  • --vis: visualize assembly results to wandb during training, may take large disk space

Logging

We use wandb for logging. Please set up your account with wandb login on the machine before running training commands.

Helper Scripts

Script for configuring and submitting jobs to cluster SLURM system:

GPUS=1 CPUS_PER_GPU=8 MEM_PER_CPU=5 QOS=normal ./scripts/sbatch_run.sh $PARTITION $JOB_NAME ./scripts/train.py --cfg_file $CFG --other_args...

Script for running a job multiple times over different random seeds:

GPUS=1 CPUS_PER_GPU=8 MEM_PER_CPU=5 QOS=normal REPEAT=$NUM_REPEAT ./scripts/dup_run_sbatch.sh $PARTITION $JOB_NAME ./scripts/train.py $CFG --other_args...

We also provide scripts for training on single/all categories of the Breaking-Bad dataset's everyday subset. See train_everyday_categories.sh and train_one_category.sh.

Testing

Similar to training, to test a pre-trained weight, simply run:

python scripts/test.py --cfg_file $CFG --weight path/to/weight

Optional auguments:

  • --category: test the model only on a subset of data
  • --min_num_part & --max_num_part: control the number of pieces we test
  • --gpus: setting testing GPUs

If you want to get per-category result of this model (currently only support everyday subset of the Breaking-Bad dataset), and report performance averaged over all the categories (used in the paper), run:

python scripts/test.py --cfg_file $CFG --weight path/to/weight --category all

We will print the metrics on each category and the averaged results.

We also provide script to gather results trained under multiple random seeds. Suppose you train the models per category by running ./scrips/train_everyday_categories.sh $COMMAND $CFG.py. Then the model checkpoint will be saved in checkpoint/$CFG-$CATEGORY-dup$X. To collect the performance on each category, run:

python scripts/collect_test.py --cfg_file $CFG.py --num_dup $X --ckp_suffix checkpoint/$CFG-

The per-category results will be formatted into latex table style for the ease of paper writing.

Besides, if you train the models on all categories by running GPUS=1 CPUS_PER_GPU=8 MEM_PER_CPU=5 QOS=normal REPEAT=$NUM_REPEAT ./scripts/dup_run_sbatch.sh $PARTITION $JOB_NAME ./scripts/train.py $CFG --other_args.... Then the model checkpoint will be saved in checkpoint/$CFG-dup$X. To collect the performance, simply adding a --train_all flag:

python scripts/collect_test.py --cfg_file $CFG.py --num_dup $X --ckp_suffix checkpoint/$CFG- --train_all

You can again control the number of pieces and GPUs to use.

Visualization

To visualize the results produced by trained model, simply run:

python scrips/vis.py --cfg_file $CFG --weight path/to/weight --category $CATEGORY --vis $NUM_TO_SAVE

It will save the original meshes, input meshes after random transformation and meshes transformed by model predictions, as well as point clouds sampled from them in path/to/vis folder (same as the pre-trained weight).