PULSE-code

PULSE — Code Repository

Reference implementation, training scripts, and benchmark baselines for the PULSE dataset paper (under double-blind review at NeurIPS 2026 Evaluations & Datasets Track).

Dataset: velvet-pine-22/PULSE · Sample subset (≈285 MB): velvet-pine-22/PULSE-sample

Repository layout

PULSE-code/
├── experiments/
│   ├── data/                     # PyTorch Dataset wrappers
│   │   ├── dataset.py                  # core multi-modal dataset (T1, T2)
│   │   ├── dataset_seqpred.py          # T2 fine-grained action recognition
│   │   ├── dataset_grasp_state.py      # T3 grasp onset anticipation
│   │   ├── dataset_forecast.py         # auxiliary forecasting heads
│   │   └── dataset_signal_forecast.py  # T5 tactile-driven motion forecast
│   │
│   ├── nets/                     # Model architectures
│   │   ├── models.py                   # backbone networks (Transformer / LSTM / 1D-CNN)
│   │   ├── models_seqpred.py           # DailyActFormer (DAF) — multi-modal Transformer
│   │   ├── models_forecast.py          # forecasting heads
│   │   ├── models_forecast_priv.py     # privileged-tactile variants for T5
│   │   ├── published_models.py         # third-party model implementations
│   │   └── baselines_published/        # 7 published baselines (re-implementation)
│   │       ├── baselines.py            #   DeepConvLSTM / InceptionTime / MS-TCN / etc.
│   │       └── syncfuse.py             #   under-pressure-style multi-modal fusion
│   │
│   ├── tasks/                    # Training + evaluation entry points
│   │   ├── train_exp1.py               # T1 — scene recognition
│   │   ├── train_seqpred.py            # T2 — action recognition (DAF + ablations)
│   │   ├── train_grasp_state.py        # T3 — grasp onset anticipation
│   │   ├── train_pred_cls.py           # T3 alt classification head
│   │   ├── train_exp_missing.py        # T4 — missing-modality robustness
│   │   ├── train_signal_forecast.py    # T5 — tactile-driven motion forecasting
│   │   ├── train_signal_forecast_priv.py  # T5 privileged variants
│   │   ├── train_baselines_t1.py       # baselines for T1
│   │   ├── train_exp{2,3,4}.py         # ablation experiments
│   │   ├── train_exp_{anticipate,grip,pose,retrieval,zeroshot}.py  # auxiliary
│   │   ├── train_pred.py / train_forecast.py
│   │   ├── eval_baselines.py / eval_combined.py
│   │   └── published_baselines.py      # baseline registry
│   │
│   ├── analysis/                 # Case study, figures, data prep utilities
│   │   ├── grasp_phase_analysis.py     # case study (gaze→EMG→hand→contact cascade)
│   │   ├── modality_viz.py / analysis_figures.py / data_statistics_figure.py
│   │   ├── extract_video_features.py / extract_videomae_features.py
│   │   ├── build_taxonomy.py / generate_action_labels.py / generate_coarse_annotations.py
│   │   ├── reannotate_actions.py / gen_val_comparison.py
│   │   ├── exp_per_subject.py / check_seg_lengths.py
│   │   └── aggregate_*.py              # collate run results
│   │
│   ├── slurm/                    # 60+ SLURM launch scripts (one per main experiment)
│   │   └── run_*.sh
│   │
│   ├── taxonomy.py               # shared 18-primitive taxonomy
│   ├── s9_primitives.json
│   └── taxonomy_v3.json
│
├── scripts/                      # Top-level utilities (not task-specific)
│   ├── build_paper_tables.py     # collates results JSONs into LaTeX tables
│   ├── eval_macrof1.py / eval_subset.py / eval_topk_v3.py
│   └── dispatch_eval.sh          # batch dispatcher
│
├── LICENSE                       # MIT
├── requirements.txt              # Python deps
└── README.md

Quick start

# 1. Set up Python environment
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 2. Point at the PULSE dataset (download from HuggingFace first)
export PULSE_ROOT=/path/to/PULSE   # the dataset root (not this code repo)

# 3. Run a training entry point as a module (from the experiments/ directory)
cd experiments
python -m tasks.train_seqpred \
    --root $PULSE_ROOT \
    --modalities mocap emg eyetrack imu pressure \
    --output_dir runs/t2_daf

# 4. Reproduce paper tables (after training all benchmarks)
cd ..
python scripts/build_paper_tables.py \
    --results_root experiments/runs/ \
    --out tables/

Why python -m tasks.train_seqpred and not python tasks/train_seqpred.py? The training scripts import sibling modules (from data.dataset import …, from nets.models import …). Running with -m from the experiments/ directory makes Python treat data/, nets/, tasks/, and analysis/ as top-level packages so the imports resolve cleanly.

Reproducing the benchmark tasks

The exact command lines (with hyperparameters, seeds, GPU configs) used for every paper table are checked in under experiments/slurm/run_*.sh, one SLURM script per paper experiment. Output JSON files from these runs are collated into LaTeX tables by scripts/build_paper_tables.py.

Hardware

Headline experiments were run on NVIDIA A800 (80 GB) GPUs. A single seed of DailyActFormer T2 trains in ~6 hours on one A800. Most baselines fit on a single 24 GB consumer GPU.

License & attribution

Code is released under MIT (see LICENSE). The PULSE dataset itself is released under CC BY-NC 4.0 (see the dataset repository).

Citation

@inproceedings{anonymous2026pulse,
  title     = {PULSE: A Synchronized Five-Modality Dataset for Multi-Modal Daily Activity Understanding},
  author    = {Anonymous Authors},
  booktitle = {Submitted to NeurIPS 2026 Evaluations and Datasets Track},
  year      = {2026},
  note      = {Under double-blind review}
}