143 lines
3.9 KiB
Markdown
143 lines
3.9 KiB
Markdown
# Scripts
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This directory contains the public Wall-X command-line helpers. Run the examples
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below from the repository root, using `python scripts/...` and `bash scripts/...`.
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Pass file and directory paths explicitly.
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## Inference smoke test
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Use `fake_inference.py` to verify that a checkpoint can be loaded and can
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produce one action chunk from a synthetic LIBERO-style observation.
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```bash
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python scripts/fake_inference.py --checkpoint-path /path/to/checkpoint
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```
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If the training config is not stored next to the checkpoint as `config.yml` or
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`config.yaml`, pass it explicitly:
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```bash
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python scripts/fake_inference.py \
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--checkpoint-path /path/to/checkpoint \
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--train-config-path /path/to/config.yml
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```
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## LIBERO evaluation
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`run_libero.sh` is a small shell wrapper around `infer_libero.py`. It requires
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the optional LIBERO simulator stack:
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```bash
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pip install -r requirements-libero.txt
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mkdir -p third_party
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git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git third_party/LIBERO
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```
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The launcher checks for LIBERO, robosuite, MuJoCo, PyOpenGL, BDDL, Gym, and
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h5py before loading the model. If LIBERO is cloned elsewhere, pass
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`LIBERO_PATH=/path/to/LIBERO`.
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```bash
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bash scripts/run_libero.sh /path/to/checkpoint
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```
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Useful environment variables:
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```bash
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CHECKPOINT_PATH=/path/to/checkpoint
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TRAIN_CONFIG_PATH=/path/to/config.yml
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TASK_SUITE_NAME=libero_spatial
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TASK_INDICES=0,1,2
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NUM_TRIALS_PER_TASK=50
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CUDA_ID=0
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SMOKE=1
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MAX_INFER_TIMES=52
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```
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`MAX_INFER_TIMES` is optional. When omitted, the launcher uses suite-specific
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defaults aligned with the LIBERO evaluator: spatial 22, object 28, goal 30,
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libero_10 52, and libero_90 40 action chunks.
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For full control, call the Python entry directly:
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```bash
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python scripts/infer_libero.py \
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--checkpoint-path /path/to/checkpoint \
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--task-suite-name libero_spatial \
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--num-trials-per-task 50 \
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--driver-mode in_process
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```
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You can also pass a complete eval config:
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```bash
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python scripts/infer_libero.py --config /path/to/eval_config.yml
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```
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## WebSocket serving
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`run_serving.sh` launches the Wall-X WebSocket server through the public
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vendored serving runtime. Pass paths explicitly; the script has no built-in
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checkpoint path.
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```bash
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bash scripts/run_serving.sh \
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--checkpoint-path /path/to/checkpoint \
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--train-config-path /path/to/config.yml \
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--port 32195
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```
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By default the script returns raw model action chunks, which is the expected
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mode for open-loop plotting. Pass `--serialize-actions` when your client expects
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robot-serialized actions.
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Useful options:
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```bash
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CUDA_ID=0
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ACTION_HORIZON=32
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IMAGE_PASSING_MODE=base64
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MAX_BATCH_SIZE=1
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```
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Additional `launch_serving.py` arguments can be forwarded after `--`:
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```bash
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bash scripts/run_serving.sh --checkpoint-path /path/to/checkpoint -- \
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--model-config.norm-key libero_all
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```
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## Open-loop WebSocket evaluation
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`draw_openloop_plot.py` compares predicted action chunks from a running
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WebSocket server against LeRobot dataset ground truth. `--dataset-root` and
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`--train-config` are required and have no built-in default.
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```bash
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python scripts/draw_openloop_plot.py \
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--uri ws://127.0.0.1:32195 \
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--dataset-root /path/to/lerobot_dataset \
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--train-config /path/to/train_config.yml \
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--episode-indices 0,1,2 \
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--save-dir ./openloop_plots
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```
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## Dataset and checkpoint utilities
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- `compute_norm_stats.py`: compute action normalization statistics for a
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local LeRobot v3 dataset. The script reads state/action parquet columns
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directly when available, so image and video columns are not decoded.
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- `merge_sharded_weights.py`: merge FSDP sharded checkpoint files into a single
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checkpoint directory.
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- `merge_tokenizer.py`: merge FAST action tokens into a Qwen2.5-VL processor
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tokenizer.
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```bash
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python scripts/merge_tokenizer.py \
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--processor-path /path/to/Qwen2.5-VL-3B-Instruct \
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--action-tokenizer-path /path/to/fast_tokenizer \
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--output-dir /path/to/merged_processor
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```
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Most scripts support `--help` for their command-line options.
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