Package folder structure (#1417)
* Move files * Replace imports & paths * Update relative paths * Update doc symlinks * Update instructions paths * Fix imports * Update grpc files * Update more instructions * Downgrade grpc-tools * Update manifest * Update more paths * Update config paths * Update CI paths * Update bandit exclusions * Remove walkthrough section
This commit is contained in:
Simon Alibert
42
README.md
42
README.md
@@ -149,44 +149,20 @@ wandb login
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(note: you will also need to enable WandB in the configuration. See below.)
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## Walkthrough
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```
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.
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├── examples # contains demonstration examples, start here to learn about LeRobot
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| └── advanced # contains even more examples for those who have mastered the basics
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├── lerobot
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| ├── configs # contains config classes with all options that you can override in the command line
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| ├── common # contains classes and utilities
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| | ├── datasets # various datasets of human demonstrations: aloha, pusht, xarm
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| | ├── envs # various sim environments: aloha, pusht, xarm
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| | ├── policies # various policies: act, diffusion, tdmpc
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| | ├── robot_devices # various real devices: dynamixel motors, opencv cameras, koch robots
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| | └── utils # various utilities
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| └── scripts # contains functions to execute via command line
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| ├── eval.py # load policy and evaluate it on an environment
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| ├── train.py # train a policy via imitation learning and/or reinforcement learning
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| ├── control_robot.py # teleoperate a real robot, record data, run a policy
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| ├── push_dataset_to_hub.py # convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub
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| └── visualize_dataset.py # load a dataset and render its demonstrations
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├── outputs # contains results of scripts execution: logs, videos, model checkpoints
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└── tests # contains pytest utilities for continuous integration
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```
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### Visualize datasets
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Check out [example 1](./examples/1_load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
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You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
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```bash
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python lerobot/scripts/visualize_dataset.py \
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python -m lerobot.scripts.visualize_dataset \
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--repo-id lerobot/pusht \
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--episode-index 0
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```
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or from a dataset in a local folder with the `root` option and the `--local-files-only` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
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```bash
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python lerobot/scripts/visualize_dataset.py \
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python -m lerobot.scripts.visualize_dataset \
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--repo-id lerobot/pusht \
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--root ./my_local_data_dir \
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--local-files-only 1 \
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@@ -199,7 +175,7 @@ It will open `rerun.io` and display the camera streams, robot states and actions
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https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
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Our script can also visualize datasets stored on a distant server. See `python lerobot/scripts/visualize_dataset.py --help` for more instructions.
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Our script can also visualize datasets stored on a distant server. See `python -m lerobot.scripts.visualize_dataset --help` for more instructions.
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### The `LeRobotDataset` format
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@@ -252,7 +228,7 @@ Check out [example 2](./examples/2_evaluate_pretrained_policy.py) that illustrat
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We also provide a more capable script to parallelize the evaluation over multiple environments during the same rollout. Here is an example with a pretrained model hosted on [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht):
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```bash
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python lerobot/scripts/eval.py \
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python -m lerobot.scripts.eval \
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--policy.path=lerobot/diffusion_pusht \
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--env.type=pusht \
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--eval.batch_size=10 \
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@@ -264,10 +240,10 @@ python lerobot/scripts/eval.py \
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Note: After training your own policy, you can re-evaluate the checkpoints with:
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```bash
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python lerobot/scripts/eval.py --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
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python -m lerobot.scripts.eval --policy.path={OUTPUT_DIR}/checkpoints/last/pretrained_model
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```
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See `python lerobot/scripts/eval.py --help` for more instructions.
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See `python -m lerobot.scripts.eval --help` for more instructions.
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### Train your own policy
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@@ -279,14 +255,14 @@ A link to the wandb logs for the run will also show up in yellow in your termina
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Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python lerobot/scripts/eval.py --help` for more instructions.
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Note: For efficiency, during training every checkpoint is evaluated on a low number of episodes. You may use `--eval.n_episodes=500` to evaluate on more episodes than the default. Or, after training, you may want to re-evaluate your best checkpoints on more episodes or change the evaluation settings. See `python -m lerobot.scripts.eval --help` for more instructions.
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#### Reproduce state-of-the-art (SOTA)
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We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
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You can reproduce their training by loading the config from their run. Simply running:
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```bash
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python lerobot/scripts/train.py --config_path=lerobot/diffusion_pusht
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python -m lerobot.scripts.train --config_path=lerobot/diffusion_pusht
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```
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reproduces SOTA results for Diffusion Policy on the PushT task.
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@@ -312,7 +288,7 @@ python lerobot/scripts/push_dataset_to_hub.py \
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See `python lerobot/scripts/push_dataset_to_hub.py --help` for more instructions.
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If your dataset format is not supported, implement your own in `lerobot/common/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
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If your dataset format is not supported, implement your own in `lerobot/datasets/push_dataset_to_hub/${raw_format}_format.py` by copying examples like [pusht_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/pusht_zarr_format.py), [umi_zarr](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/umi_zarr_format.py), [aloha_hdf5](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/aloha_hdf5_format.py), or [xarm_pkl](https://github.com/huggingface/lerobot/blob/main/lerobot/datasets/push_dataset_to_hub/xarm_pkl_format.py). -->
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### Add a pretrained policy
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