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Refactor datasets into LeRobotDataset (#91)

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Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
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Remi
2024-04-25 12:23:12 +02:00
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commit 659c69a1c0
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README.md
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@@ -118,30 +118,7 @@ wandb login
### Visualize datasets
You can import our dataset class, download the data from the HuggingFace hub and use our rendering utilities:
```python
""" Copy pasted from `examples/1_visualize_dataset.py` """
import os
from pathlib import Path
import lerobot
from lerobot.common.datasets.aloha import AlohaDataset
from lerobot.scripts.visualize_dataset import render_dataset
print(lerobot.available_datasets)
# >>> ['aloha_sim_insertion_human', 'aloha_sim_insertion_scripted', 'aloha_sim_transfer_cube_human', 'aloha_sim_transfer_cube_scripted', 'pusht', 'xarm_lift_medium']
# TODO(rcadene): remove DATA_DIR
dataset = AlohaDataset("pusht", root=Path(os.environ.get("DATA_DIR")))
video_paths = render_dataset(
dataset,
out_dir="outputs/visualize_dataset/example",
max_num_episodes=1,
)
print(video_paths)
# ['outputs/visualize_dataset/example/episode_0.mp4']
```
Check out [examples](./examples) to see how you can import our dataset class, download the data from the HuggingFace hub and use our rendering utilities.
Or you can achieve the same result by executing our script from the command line:
```bash
@@ -153,7 +130,7 @@ hydra.run.dir=outputs/visualize_dataset/example
### Evaluate a pretrained policy
Check out [example 2](./examples/2_evaluate_pretrained_policy.py) to see how you can load a pretrained policy from HuggingFace hub, load up the corresponding environment and model, and run an evaluation.
Check out [examples](./examples) to see how you can load a pretrained policy from HuggingFace hub, load up the corresponding environment and model, and run an evaluation.
Or you can achieve the same result by executing our script from the command line:
```bash
@@ -176,24 +153,30 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
### Train your own policy
You can import our dataset, environment, policy classes, and use our training utilities (if some data is missing, it will be automatically downloaded from HuggingFace hub): check out [example 3](./examples/3_train_policy.py). After you run this, you may want to revisit [example 2](./examples/2_evaluate_pretrained_policy.py) to evaluate your training output!
Check out [examples](./examples) to see how you can start training a model on a dataset, which will be automatically downloaded if needed.
In general, you can use our training script to easily train any policy on any environment:
```bash
python lerobot/scripts/train.py \
env=aloha \
task=sim_insertion \
dataset_id=aloha_sim_insertion_scripted \
repo_id=lerobot/aloha_sim_insertion_scripted \
policy=act \
hydra.run.dir=outputs/train/aloha_act
```
After training, you may want to revisit model evaluation to change the evaluation settings. In fact, during training every checkpoint is already evaluated but on a low number of episodes for efficiency. Check out [example](./examples) to evaluate any model checkpoint on more episodes to increase statistical significance.
## Contribute
If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
### Add a new dataset
```python
# TODO(rcadene, AdilZouitine): rewrite this section
```
To add a dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
```bash
huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
@@ -255,6 +238,10 @@ python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir
### Add a pretrained policy
```python
# TODO(rcadene, alexander-soare): rewrite this section
```
Once you have trained a policy you may upload it to the HuggingFace hub.
Firstly, make sure you have a model repository set up on the hub. The hub ID looks like HF_USER/REPO_NAME.