OpenGait/datasets/Scoliosis1K

Tutorial for Scoliosis1K

Download the Scoliosis1K Dataset

You can download the dataset from the official website. The dataset is provided as four compressed files:

• Scoliosis1K-sil-raw.zip
• Scoliosis1K-sil-pkl.zip
• Scoliosis1K-pose-raw.zip
• Scoliosis1K-pose-pkl.zip

We recommend using the provided pickle (.pkl) files for convenience. Decompress them with the following commands:

unzip -P <password> Scoliosis1K-sil-pkl.zip
unzip -P <password> Scoliosis1K-pose-pkl.zip

Note

: The <password> can be obtained by signing the release agreement and sending it to 12331257@mail.sustech.edu.cn.

Dataset Structure

After decompression, you will get the following structure:

├── Scoliosis1K-sil-pkl
│   ├── 00000                     # Identity
│   │   ├── Positive              # Class
│   │   │   ├── 000_180           # View
│   │   │   └── 000_180.pkl       # Estimated Silhouette (PP-HumanSeg v2)
│
├── Scoliosis1K-pose-pkl
│   ├── 00000                     # Identity
│   │   ├── Positive              # Class
│   │   │   ├── 000_180           # View
│   │   │   └── 000_180.pkl       # Estimated 2D Pose (ViTPose)

Processing from RAW Dataset (optional)

If you prefer, you can process the raw dataset into .pkl format.

# For silhouette raw data
python datasets/pretreatment.py --input_path=<path_to_raw_silhouettes> --output_path=<output_path>

# For pose raw data
python datasets/pretreatment.py --input_path=<path_to_raw_pose> --output_path=<output_path> --pose --dataset=OUMVLP

---

Training and Testing

Before training or testing, modify the dataset_root field in configs/sconet/sconet_scoliosis1k.yaml.

Then run the following commands:

# Training
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 \
opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase train --log_to_file

# Testing
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 \
opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase test --log_to_file

Fixed-pool ratio comparison

If you want to compare 1:1:2 against 1:1:8 without changing the evaluation pool, do not compare Scoliosis1K_112.json against Scoliosis1K_118.json directly. Those two files differ substantially in train/test membership.

For a cleaner same-pool comparison, use:

• datasets/Scoliosis1K/Scoliosis1K_118.json
  • original 1:1:8 split
• datasets/Scoliosis1K/Scoliosis1K_118_fixedpool_train112.json
  • same TEST_SET as 118
  • same positive/neutral TRAIN_SET ids as 118
  • downsampled TRAIN_SET negatives to 148, giving train counts 74 positive / 74 neutral / 148 negative

The helper used to generate that derived partition is:

uv run python scripts/build_scoliosis_fixedpool_partition.py \
  --base-partition datasets/Scoliosis1K/Scoliosis1K_118.json \
  --dataset-root /mnt/public/data/Scoliosis1K/Scoliosis1K-sil-pkl \
  --negative-multiplier 2 \
  --output-path datasets/Scoliosis1K/Scoliosis1K_118_fixedpool_train112.json \
  --seed 118

Modality sanity check

The silhouette and skeleton-map pipelines are different experiments and should not be mixed when you interpret results.

• Scoliosis1K-sil-pkl is the silhouette modality used by the standard ScoNet configs.
• pose-derived heatmap roots such as Scoliosis1K_sigma_8.0/pkl or DRF exports are skeleton-map inputs and require in_channel: 2.
• DRF does not use the silhouette stream as an input. It uses 0_heatmap.pkl plus 1_pav.pkl.

Naming note:

• in this repo, the local ScoNet training config and model class are usually the paper's ScoNet-MT, not the CE-only paper ScoNet
• in these docs, ScoNet-MT-ske means the skeleton-map variant of that same model path
• checkpoint filenames like ScoNet-20000-better.pt do not identify the modality by name alone

A strong silhouette checkpoint does not validate the skeleton-map path. In particular, ckpt/ScoNet-20000-better.pt is a silhouette checkpoint:

• its first convolution expects 1-channel input
• the matching eval config points to Scoliosis1K-sil-pkl

So if you are debugging DRF or ScoNet-MT-ske reproduction, do not use ScoNet-20000-better.pt as evidence that the heatmap preprocessing is correct.

Overlay caveat

Do not treat a direct overlay between Scoliosis1K-sil-pkl and pose-derived skeleton maps as a valid alignment test.

Reason:

• the released silhouette modality is an estimated segmentation output from PP-HumanSeg v2
• the released pose modality is an estimated keypoint output from ViTPose
• the two modalities are normalized by different preprocessing pipelines before they reach OpenGait

So a silhouette-vs-skeleton mismatch in a debug figure is usually a cross-modality frame-of-reference issue, not proof that the raw dataset is bad. The more important check for skeleton-map debugging is whether the limb and joint channels align with each other inside 0_heatmap.pkl.

---

Pose-to-Heatmap Conversion

From our paper: Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening (MICCAI 2025)

CUDA_VISIBLE_DEVICES=0,1,2,3 \
python -m torch.distributed.launch --nproc_per_node=4 \
datasets/pretreatment_heatmap.py \
  --pose_data_path=<path_to_pose_pkl> \
  --save_root=<output_path> \
  --dataset_name=OUMVLP

DRF Preprocessing

For the DRF model, OpenGait expects a combined runtime dataset with:

• 0_heatmap.pkl: the two-channel skeleton map sequence
• 1_pav.pkl: the paper-style Postural Asymmetry Vector (PAV), repeated along the sequence axis so it matches OpenGait's multi-input loader contract

The PAV pass is implemented from the paper:

1. convert pose to COCO17 if needed
2. pad missing joints
3. pelvis-center and height normalize the sequence
4. compute vertical, midline, and angular deviations for the 8 symmetric joint pairs
5. apply IQR filtering per metric
6. average over time
7. min-max normalize across the full dataset (paper default), or across TRAIN_SET when --stats_partition is provided as an anti-leakage variant

Run:

uv run python datasets/pretreatment_scoliosis_drf.py \
  --pose_data_path=<path_to_pose_pkl> \
  --output_path=<path_to_drf_pkl>

The script uses configs/drf/pretreatment_heatmap_drf.yaml by default. That keeps the upstream OpenGait/SkeletonGait heatmap behavior from commit f754f6f3831e9f83bb28f4e2f63dd43d8bcf9dc4 for the skeleton-map branch while still building the DRF-specific two-channel output.

If you explicitly want the more paper-literal summed heatmap ablation, add:

  --heatmap_reduction=sum

If you explicitly want train-only PAV min-max statistics, add:

  --stats_partition=./datasets/Scoliosis1K/Scoliosis1K_118.json

Heatmap debugging notes

Current confirmed findings from local debugging:

• the raw pose dataset itself looks healthy; poor ScoNet-MT-ske results are not explained by obvious missing-joint collapse
• a larger heatmap sigma can materially blur away the articulated structure; sigma=8 was much broader than the silhouette geometry, while smaller sigma values recovered more structure
• an earlier bug aligned the limb and joint channels separately; that made the two channels of 0_heatmap.pkl slightly misregistered
• the heatmap path is now patched so limb and joint channels share one alignment crop
• the heatmap aligner now also supports align_args.scope: sequence, which applies one shared crop box to the whole sequence instead of recomputing it frame by frame
• the heatmap config can also rebalance the two channels after alignment with channel_gain_limb / channel_gain_joint; this keeps the crop geometry fixed while changing limb-vs-joint strength

Remaining caution:

• the exported skeleton map is stored as 64x64
• if the runtime config uses BaseSilCuttingTransform, the network actually sees 64x44
• that symmetric left/right crop is not automatically wrong, but it is still a meaningful ablation point for skeleton-map experiments

The output layout is:

<path_to_drf_pkl>/
├── pav_stats.pkl
├── 00000/
│   ├── Positive/
│   │   ├── 000_180/
│   │   │   ├── 0_heatmap.pkl
│   │   │   └── 1_pav.pkl

Point configs/drf/drf_scoliosis1k.yaml:data_cfg.dataset_root to this output directory before training or testing.

DRF Training and Testing

CUDA_VISIBLE_DEVICES=0,1,2,3 \
uv run python -m torch.distributed.launch --nproc_per_node=4 \
opengait/main.py --cfgs configs/drf/drf_scoliosis1k.yaml --phase train

CUDA_VISIBLE_DEVICES=0,1,2,3 \
uv run python -m torch.distributed.launch --nproc_per_node=4 \
opengait/main.py --cfgs configs/drf/drf_scoliosis1k.yaml --phase test