Test

• In the folder ./data, run tar xfvz sample.tgz to get:

  • sample time projection chamber (TPC) frame data (sample_0.npy to sample_7.npy);
  • manifest file test.txt used for loading sample data;
  • cached compressed data (compressed_0.npy to compressed_7.npy);
  • cached decompressed data (decompressed_0.npy to decompressed_7.npy);
  • expected reconstruction errors (sample_metric_results.csv)

  The cached network output and expected reconstruction error file are used for benchmarking.

• Pretrained encoder and decoder .pth files are saved in ./checkpoints.

• To run the pretrained model and compare the result, run python test.py.

• The output will be saved to the folder ./results.

Training

• Usage: python train.py One can modified the parameters inside train.py.

Torch-script pretrained model for C++

• Install neuralcompressor: python setup.py develop --user
• Usage examples:
  • python neuralcompress/utils/bcae_scriptor.py --checkpoint_path checkpoints --epoch 2000 --save_path torchscript/
• Parameters:
  • --checkpoint_path: The path to the checkpoints.
  • --epoch: The epoch of the pretrained checkpoints to load.
  • --save_path: The path to save the scripted encoder and decoder.
  • --prefix: Prefix to the filename of the scripted encoder and decoder | default=bcae.

Inference

Produce compressed codes of each input TPC frame.

• Usage examples:

  • python inference.py --data_size 8 --batch_size 4 --partition test --random --checkpoint_path ./checkpoints/ --epoch 2000 --save_path inference_results --half

• Parameters:

  • --data_path: The path to data.
  • --device: Choose from {cuda,cpu}. The device to run the inference | default=cuda.
  • --data_size: Number of frames to load | default=1.
  • --batch_size: Batch size | default=1.
  • --partition: Choose from {train,valid,test} partition from which to load the data | default=test.
  • --random: Whether to get a random sample.
  • --checkpoint_path: The path to the pretrained checkpoints.
  • --epoch: The epoch of the pretrained checkpoints to load.
  • --save_path: The path to save output tensor.
  • --half: Whether to save the output with half precision.
  • --prefix: Output file prefix | default=output.

GPU benchmarking

• Usage example:

  • run on dummy data without data loader: python benchmark/gpu_inference.py checkpoints/encoder_2000.pt.
  • run on real data with data location and loader: python benchmark/gpu_inference checkpoints/encoder_2000.pt --with_loader --data_root ./data.
  • run with half-precision and adjust data loading workers with flags --half_precision and --num_workers 8.

• Parameters:

  • positional arguments:

    • checkpoint: The path to the encoder pt file.

  • optional arguments:

    • --num_runs: Number of runs to calculate the run-time. (default=10)
    • --benchmark: If used, set torch.backends.cudnn.benchmark to True.
    • --with_loader: If used, use TPC dataloader. Use randomly generated data if otherwise
    • --data_root: Path to data, when loading data with TPC dataloader (--with_loader). (default=None)
    • --data_size: Number of frames to load. (default=1)
    • --batch_size: Batch size. (default=1)
    • --pin_memory: If used, the dataloader will copy Tensors into CUDA pinned memory before returning them.
    • --num_workers: Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default=0)
    • --prefetch_factor: Number of samples loaded in advance by each worker. (default=2)
    • --result_fname: Result filename. (default=result.csv)
    • --half_precision: If used, run inference with half precision (float16)

Reference

@INPROCEEDINGS{huang2021bcae,
    author={Huang, Yi and Ren, Yihui and Yoo, Shinjae and Huang, Jin},
    booktitle={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},
    title={Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder},
    year={2021},
    volume={},
    number={},
    pages={1094-1099},
    doi={10.1109/ICMLA52953.2021.00179}
}