spatialencoder_full

Full SpatialEncoder training dataset prepared on 2026-05-25.

The original relative file layout under the local data root is preserved. Manifests and preparation stats are stored under metadata/spatialencoder_full_20260525/.

Training items:

• CA-1M: 2966
• hyperism: 560
• ADT: 64
• Manifest entries: 391760
• Logical size excluding directory entries: 2044.28 GiB
• Directory entries: 64

Goal

This dataset is intended to become the BOX_DATA_PATH / BOX_DATA_VAL_PATH input tree used by SpatialEncoder training.

After preparation, the training code should see:

${BOX_DATA_PATH}/
├── CA-1M/
│   ├── train/
│   │   └── ca1m-train-<video_id>.tar
│   ├── val/
│   │   └── ca1m-val-<video_id>.tar
│   └── val-unzip/
├── hyperism/
│   └── hyperism/
├── aria_digital_twin/
│   └── ADT/
├── pickle/
│   └── CA-1M/
│       └── *train*.pkl
├── BoxFromMotion/
│   └── dataset/
│       ├── CA-1M.json
│       ├── hyperism.json
│       └── ADT.json
├── json_wo_pose/
└── val-json/

Use:

export BOX_DATA_PATH=/path/to/spatialencoder_full
export BOX_DATA_VAL_PATH=/path/to/spatialencoder_full/BoxFromMotion/dataset

1. Download

Install the Hugging Face CLI if needed:

pip install -U "huggingface_hub[cli]"

Download the dataset while preserving repository paths. The expanded ADT and Hyperism frame trees are not stored directly in the repository; download the archive shards and restore them locally.

• ADT archives: aria_digital_twin/ADT_tars/*.tar
• Hyperism archives: hyperism_required_shards/*.tar

DATA_ROOT=/mnt/nvme6/jieneng/data/spatialencoder_full
mkdir -p "$DATA_ROOT"

hf download qicq1c/spatialencoder_full \
  --type dataset \
  --local-dir "$DATA_ROOT" \
  --exclude "aria_digital_twin/ADT/**" \
  --exclude "hyperism/hyperism/unzip/**"

The ADT tar archives are about 405 GiB. After extraction, aria_digital_twin/ADT/ is about 406 GiB. The Hyperism tar shards are about 35.6 GiB. Keep the archives and expanded trees if you want the download to be resumable and reproducible.

2. Restore ADT From Tar Archives

Restore the ADT frame tree from the downloaded tar archives:

cd "$DATA_ROOT"
mkdir -p aria_digital_twin/ADT

for tar_file in aria_digital_twin/ADT_tars/*.tar; do
  tar --skip-old-files -xf "$tar_file" -C aria_digital_twin/ADT
done

This recreates paths such as:

aria_digital_twin/ADT/Apartment_release_clean_seq131_M1292/depth_frames/...
aria_digital_twin/ADT/Apartment_release_clean_seq131_M1292/rgb_frames/...

3. Restore Hyperism From Tar Shards

Restore the Hyperism frame tree from the downloaded tar shards:

cd "$DATA_ROOT"
mkdir -p hyperism

for shard in hyperism_required_shards/*.tar; do
  tar --skip-old-files -xf "$shard" -C hyperism
done

This recreates paths such as:

hyperism/hyperism/unzip/ai_001_001/...
hyperism/hyperism/unzip/ai_001_002/...

4. Expand Packed Add-Ons If Present

If the download contains archive files such as pickle.zip, hyperism-train-json.zip, or hyperism-val-json.zip, unzip them at the data root:

cd "$DATA_ROOT"

for z in pickle.zip hyperism-train-json.zip hyperism-val-json.zip; do
  if [ -f "$z" ]; then
    unzip -o "$z" -d "$DATA_ROOT"
  fi
done

Skip this step for files that are already expanded in the final tree.

5. Check Required Files

Run these checks before training:

export BOX_DATA_PATH="$DATA_ROOT"
export BOX_DATA_VAL_PATH="$DATA_ROOT/BoxFromMotion/dataset"

test -d "$BOX_DATA_PATH/CA-1M/train"
test -d "$BOX_DATA_PATH/CA-1M/val"
test -d "$BOX_DATA_PATH/pickle/CA-1M"
test -d "$BOX_DATA_PATH/hyperism/hyperism"
test -d "$BOX_DATA_PATH/aria_digital_twin/ADT"

test -f "$BOX_DATA_VAL_PATH/CA-1M.json"
test -f "$BOX_DATA_VAL_PATH/hyperism.json"
test -f "$BOX_DATA_VAL_PATH/ADT.json"

find "$BOX_DATA_PATH/CA-1M/train" -name 'ca1m-train-*.tar' | wc -l
find "$BOX_DATA_PATH/pickle/CA-1M" -name '*train*.pkl' | wc -l

Expected minimum result:

• CA-1M/train contains many ca1m-train-*.tar files.
• pickle/CA-1M contains CA-1M iterable training metadata.
• BoxFromMotion/dataset/{CA-1M,hyperism,ADT}.json exist.
• Hyperism and ADT frame paths referenced by the json files exist under BOX_DATA_PATH.

6. Set Training Environment

From the SpatialEncoder code checkout:

cd /path/to/SpatialEncoder

export BOX_DATA_PATH=/path/to/spatialencoder_full
export BOX_DATA_VAL_PATH=/path/to/spatialencoder_full/BoxFromMotion/dataset
export BOX_WEIGHTS_PATH=/path/to/training_output
export SAM3_CHECKPOINT=$BOX_WEIGHTS_PATH/sam3.1_multiplex.pt

export PYTORCH_ALLOC_CONF=expandable_segments:True
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
export NUMEXPR_NUM_THREADS=1
export NCCL_DEBUG=WARN
export TORCH_NCCL_BLOCKING_WAIT=1

Download the SAM 3.1 checkpoint separately into BOX_WEIGHTS_PATH:

wget -P "$BOX_WEIGHTS_PATH" \
  --header="Authorization: Bearer YOUR_HF_TOKEN" \
  https://huggingface.co/facebook/sam3.1/resolve/main/sam3.1_multiplex.pt

7. Dataset Smoke Tests

The merged training config samples datasets according to:

trainer.data.train.dataset.weights = [CA-1M, hyperism, ADT]

Before starting a long run, verify each dataset can print loss:

# CA-1M only
trainer.data.train.dataset.weights='[1,0,0]'

# Hyperism only
trainer.data.train.dataset.weights='[0,1,0]'

# ADT only
trainer.data.train.dataset.weights='[0,0,1]'

Example 8-GPU smoke command:

RUN_NAME=SpatialEncoder_smoke_$(date +%Y%m%d_%H%M%S)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 env -u LD_LIBRARY_PATH python sam3/train/train.py \
  -c configs/depth/train_merged_iterable_da3_best_memory_extras_lowmem_actckpt_fa3.yaml \
  --use-cluster 0 \
  --num-gpus 8 \
  paths.experiment_log_dir="$BOX_WEIGHTS_PATH/Exps/$RUN_NAME" \
  trainer.model.use_fa3=true \
  trainer.distributed.gradient_as_bucket_view=false \
  trainer.data.train.dataset.weights='[0,1,0]' \
  trainer.logging.log_freq=1 \
  trainer.logging.log_scalar_frequency=1

It is ready if the log reaches lines like:

Train Epoch: [0][   0/...] ... Losses/train_all_loss: ...

The first batch can be slow because workers are filling caches. Later steps should have near-zero Data Time.

8. Mixed Training

Once all three single-dataset smoke tests print loss, launch the mixed run:

RUN_NAME=SpatialEncoder_mixed_8gpu_$(date +%Y%m%d_%H%M%S)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 env -u LD_LIBRARY_PATH python sam3/train/train.py \
  -c configs/depth/train_merged_iterable_da3_best_memory_extras_lowmem_actckpt_fa3.yaml \
  --use-cluster 0 \
  --num-gpus 8 \
  paths.experiment_log_dir="$BOX_WEIGHTS_PATH/Exps/$RUN_NAME" \
  trainer.model.use_fa3=true \
  trainer.distributed.gradient_as_bucket_view=false \
  trainer.data.train.dataset.weights='[0.4,0.2,0.4]' \
  trainer.logging.log_freq=1 \
  trainer.logging.log_scalar_frequency=1

For quick debugging on slow storage, temporarily add:

scratch.num_train_workers=2

For the default full setting, omit that override; the config uses scratch.num_train_workers=16.

Troubleshooting

• If training appears stuck before the first loss, check whether dataloader workers are still starting. With num_train_workers=16, the first batch can take around 1-2 minutes on large mixed data.
• If only one dataset fails, rerun with the corresponding one-hot weight to isolate missing files.
• If use_fa3=true fails at import or CUDA runtime, retry with trainer.model.use_fa3=false to separate data issues from FA3 compatibility issues.
• If a run is interrupted, kill the whole process group and confirm GPUs are free with:

nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits