Update training-ready data preparation steps

README.md

@@ -12,3 +12,233 @@ Training items:
 - 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:
+
+```text
+${BOX_DATA_PATH}/
+├── CA-1M/
+│   ├── train/
+│   │   └── ca1m-train-<video_id>.tar
+│   ├── val/
+│   │   └── ca1m-val-<video_id>.tar
+│   └── val-unzip/
+├── hyperism/
+│   └── hyperism/
+├── aria_digital_twin/
+│   └── ADT/
+├── pickle/
+│   └── CA-1M/
+│       └── *train*.pkl
+├── BoxFromMotion/
+│   └── dataset/
+│       ├── CA-1M.json
+│       ├── hyperism.json
+│       └── ADT.json
+├── json_wo_pose/
+└── val-json/
+```
+
+Use:
+
+```bash
+export BOX_DATA_PATH=/path/to/spatialencoder_full
+export BOX_DATA_VAL_PATH=/path/to/spatialencoder_full/BoxFromMotion/dataset
+```
+
+## 1. Download
+
+Install the Hugging Face CLI if needed:
+
+```bash
+pip install -U "huggingface_hub[cli]"
+```
+
+Download the dataset while preserving repository paths:
+
+```bash
+DATA_ROOT=/mnt/nvme6/jieneng/data/spatialencoder_full
+mkdir -p "$DATA_ROOT"
+
+huggingface-cli download qicq1c/spatialencoder_full \
+  --repo-type dataset \
+  --local-dir "$DATA_ROOT" \
+  --local-dir-use-symlinks False
+```
+
+The full dataset is about 2 TiB, so make sure the target filesystem has enough space before starting.
+
+## 2. Expand Packed Add-Ons If Present
+
+If the download contains archive files such as `pickle.zip`, `hyperism-train-json.zip`, or `hyperism-val-json.zip`, unzip them at the data root:
+
+```bash
+cd "$DATA_ROOT"
+
+for z in pickle.zip hyperism-train-json.zip hyperism-val-json.zip; do
+  if [ -f "$z" ]; then
+    unzip -o "$z" -d "$DATA_ROOT"
+  fi
+done
+```
+
+If the download contains `hyperism_required_shards/*.tar`, expand those shards into the Hyperism frame directory:
+
+```bash
+mkdir -p "$DATA_ROOT/hyperism/hyperism/unzip"
+
+if [ -d "$DATA_ROOT/hyperism_required_shards" ]; then
+  for shard in "$DATA_ROOT"/hyperism_required_shards/*.tar; do
+    tar -xf "$shard" -C "$DATA_ROOT/hyperism/hyperism/unzip"
+  done
+fi
+```
+
+Skip this step for files that are already expanded in the final tree.
+
+## 3. Check Required Files
+
+Run these checks before training:
+
+```bash
+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`.
+
+## 4. Set Training Environment
+
+From the SpatialEncoder code checkout:
+
+```bash
+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=$BOX_WEIGHTS_PATH/sam3.1_multiplex.pt
+
+export PYTORCH_ALLOC_CONF=expandable_segments:True
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+export NCCL_DEBUG=WARN
+export TORCH_NCCL_BLOCKING_WAIT=1
+```
+
+Download the SAM 3.1 checkpoint separately into `BOX_WEIGHTS_PATH`:
+
+```bash
+wget -P "$BOX_WEIGHTS_PATH" \
+  --header="Authorization: Bearer YOUR_HF_TOKEN" \
+  https://huggingface.co/facebook/sam3.1/resolve/main/sam3.1_multiplex.pt
+```
+
+## 5. Dataset Smoke Tests
+
+The merged training config samples datasets according to:
+
+```text
+trainer.data.train.dataset.weights = [CA-1M, hyperism, ADT]
+```
+
+Before starting a long run, verify each dataset can print loss:
+
+```bash
+# 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:
+
+```bash
+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/$RUN_NAME" \
+  trainer.model.use_fa3=true \
+  trainer.distributed.gradient_as_bucket_view=false \
+  trainer.data.train.dataset.weights='[0,1,0]' \
+  trainer.logging.log_freq=1 \
+  trainer.logging.log_scalar_frequency=1
+```
+
+It is ready if the log reaches lines like:
+
+```text
+Train Epoch: [0][   0/...] ... Losses/train_all_loss: ...
+```
+
+The first batch can be slow because workers are filling caches. Later steps should have near-zero `Data Time`.
+
+## 6. Mixed Training
+
+Once all three single-dataset smoke tests print loss, launch the mixed run:
+
+```bash
+RUN_NAME=SpatialEncoder_mixed_8gpu_$(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:
+
+```bash
+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:
+
+```bash
+nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits
+```