| # Sample data — NBA (100 scenes) |
|
|
| A **tiny smoke-test subset** so you can verify the code runs end-to-end before wiring up the |
| full datasets. **Not** for reproducing any reported number. |
|
|
| ``` |
| sample_data/nba/original/nba_train.npy # (100, 30, 11, 2) float32, 258 KB |
| sample_data/nba/original/nba_test.npy # (100, 30, 11, 2) float32, 258 KB |
| ``` |
|
|
| * Layout: `(scenes, frames, agents, xy)` — 30 frames @ 5 Hz = 10 past + 20 future (4.0 s), |
| 11 agents (10 players + ball, ball = index 10), absolute court coordinates. |
| * Each file is the **exact first-100 prefix** of the corresponding full split |
| (`trajs[:100]`), which is the same slice the loaders take — so scene indices line up |
| with the full dataset. |
| * Full splits are 32 500 train / 12 500 test scenes. |
|
|
| ## Use it |
|
|
| `--data_dir` must point at the **parent** of `original/` (the loader appends `original/` itself): |
|
|
| ```bash |
| cd MoFlow |
| CUDA_VISIBLE_DEVICES=0 python fm_nba_graph_v6.py \ |
| --cfg cfg/nba/cor_fm.yml --exp smoke \ |
| --data_dir ../sample_data/nba \ |
| --n_train 100 --n_test 100 \ |
| --batch_size 8 --epochs 1 \ |
| --fm_in_scaling --tied_noise --top_n_neighbors 5 --uncertainty_weight 0.01 |
| ``` |
|
|
| Verified to load and collate: |
|
|
| ``` |
| past_traj (B, 11, 10, 6) |
| past_traj_original_scale (B, 11, 10, 6) |
| fut_traj_original_scale (B, 11, 20, 2) |
| ``` |
|
|
| > With only 100 scenes the model cannot learn anything meaningful — expect high ADE/FDE. |
| > This subset exists purely to confirm the data path, model construction, training step and |
| > evaluation loop all execute. |
|
|
| ## Provenance |
|
|
| Derived from the NBA player-tracking (SportVU) movement data as preprocessed by prior |
| trajectory-prediction work. Redistributed here only as a minimal fixture for code testing; |
| please refer to the original data source for licensing and terms of use. |
|
|
