SRA — preprocessed data for MID / LED / MoFlow

All arrays are float32, layout (scenes, frames, agents, xy), 30 frames @ 5 Hz = 10 past + 20 future (4.0 s horizon).

nba/original/nba_train.npy    (32500, 30, 11, 2)    85.8 MB
nba/original/nba_test.npy     (12500, 30, 11, 2)    33.0 MB
sport/soccer/train.npy         (7164, 30, 23, 2)    79.1 MB
sport/soccer/val.npy           (1785, 30, 23, 2)    19.7 MB
sport/football/train.npy      (37859, 30, 23, 2)   209.0 MB
sport/football/val.npy         (1000, 30, 23, 2)     5.5 MB

LED_pretrained_core/base_diffusion_model.p                63.2 MB   # NBA
LED_pretrained_core/base_diffusion_model_football.p       26.4 MB   # football
LED_pretrained_core/base_diffusion_model_soccer_psnorm.p  26.4 MB   # soccer (used by cfg)
LED_pretrained_core/base_diffusion_model_soccer.p         26.4 MB   # soccer (older, non-psnorm)

⚠️ LED additionally requires a pretrained core denoising model

MID and MoFlow train from scratch — the .npy files are all they need. LED does not. Its leapfrog design trains an initializer on top of a frozen, pretrained diffusion core, and both trainer/train_led_graph.py and trainer/train_sport_led.py hard-load it (torch.load(...) → model.load_state_dict(cp['model_dict'])). Training will crash without it.

Install to LED/results/checkpoints/, keeping the exact filenames — the paths are baked into the configs:

mkdir -p LED/results/checkpoints
cp LED_pretrained_core/*.p LED/results/checkpoints/

Config	expects
`cfg/nba/led_augment.yml`	`./results/checkpoints/base_diffusion_model.p`
`cfg/sport/football.yml`	`./results/checkpoints/base_diffusion_model_football.p`
`cfg/sport/soccer.yml`	`./results/checkpoints/base_diffusion_model_soccer_psnorm.p`

Each checkpoint contains model_dict (94 tensors) — verified loadable.

Two soccer cores are included. The configs use the _psnorm one; base_diffusion_model_soccer.p is an earlier non-psnorm core kept for reference. They are not interchangeable — swapping them changes the LED-soccer baseline.

All three hosts (MID, LED, MoFlow) use the same NBA scenes: 32 500 train / 12 500 test.

Note: the LED repo ships a larger raw nba_train.npy / nba_test.npy (40 000 / 47 940 scenes), but LED/data/dataloader_nba.py slices [:32500] / [:12500], and those slices are byte-identical to the files here (verified: maxdiff = 0.0). The extra scenes are never used, so the files below are sufficient for all three hosts.

The evaluation set for every reported NBA number is the 12 500-scene test split (matches the E5 per-scene counts: 12 500 NBA / 1 785 soccer / 1 000 football).

Where to put it

Host	Dataset	Install to	Pass as
MoFlow	NBA	`MoFlow/data/nba/original/`	`--data_dir ./data/nba` ¹
MID	NBA	reuse the same copy	`--data_dir <…>/nba/original` ²
LED	NBA	copy into `LED/data/files/`	path is hardcoded ³
MoFlow / MID	soccer, football	anywhere	`--data_dir <…>/sport/football`
LED	soccer, football	anywhere	set `data_dir` in the YAML ⁴

¹ MoFlow's NBA loader appends original/ itself — point --data_dir at the parent. ² MID's NBA script takes the original/ directory directly (opposite convention to MoFlow). ³ LED/data/dataloader_nba.py hardcodes ./data/files/nba_{train,test}.npy, so copy the two NBA files there and launch from the LED/ directory: mkdir -p LED/data/files && cp nba/original/nba_*.npy LED/data/files/ LED also rescales internally by 94/28 — do not pre-scale the files. ⁴ LED sport reads data_dir from LED/cfg/sport/{soccer,football}.yml, not the CLI.

Sport loaders expect train.npy and val.npy in the given directory.

Provenance

NBA: player-tracking (SportVU) data as preprocessed by prior trajectory-prediction work. Soccer / football: multi-agent sports tracking data (23 agents). Consult the original data sources for licensing and terms of use before redistributing.