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update README

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This commit is contained in:
Alexander Soare
2024-03-26 16:28:16 +00:00
parent 1ed0110900
commit be4441c7ff
3 changed files with 23 additions and 23 deletions
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22
README.md
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@@ -135,11 +135,7 @@ hydra.run.dir=outputs/visualize_dataset/example
### Evaluate a pretrained policy
You can import our environment class, download pretrained policies from the HuggingFace hub, and use our rollout utilities with rendering:
```python
""" Copy pasted from `examples/2_evaluate_pretrained_policy.py`
# TODO
```
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.
Or you can achieve the same result by executing our script from the command line:
```bash
@@ -150,7 +146,7 @@ eval_episodes=10 \
hydra.run.dir=outputs/eval/example_hub
```
After launching training of your own policy, you can also re-evaluate the checkpoints with:
After training your own policy, you can also re-evaluate the checkpoints with:
```bash
python lerobot/scripts/eval.py \
--config PATH/TO/FOLDER/config.yaml \
@@ -163,19 +159,9 @@ 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):
```python
""" Copy pasted from `examples/3_train_policy.py`
# TODO
```
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!
Or you can achieve the same result by executing our script from the command line:
```bash
python lerobot/scripts/train.py \
hydra.run.dir=outputs/train/example
```
You can easily train any policy on any environment:
In general, you can use our training script to easily train any policy on any environment:
```bash
python lerobot/scripts/train.py \
env=aloha \

2
examples/2_evaluate_pretrained_policy.py
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@@ -14,7 +14,7 @@ from lerobot.scripts.eval import eval
hub_id = "lerobot/diffusion_policy_pusht_image"
folder = Path(snapshot_download(hub_id))
# OR uncomment the following to evaluate a policy from the local outputs/train folder.
folder = Path("outputs/train/example_pusht_diffusion")
# folder = Path("outputs/train/example_pusht_diffusion")
config_path = folder / "config.yaml"
weights_path = folder / "model.pt"

22
tests/test_examples.py
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@@ -46,11 +46,25 @@ def test_examples_3_and_2():
with open(path, "r") as file:
file_contents = file.read()
# Do less evals and use CPU.
# Do less evals, use CPU, and use the local model.
file_contents = _find_and_replace(
file_contents,
['"eval_episodes=10"', '"rollout_batch_size=10"', '"device=cuda"'],
['"eval_episodes=1"', '"rollout_batch_size=1"','"device=cpu"'],
[
'"eval_episodes=10"',
'"rollout_batch_size=10"',
'"device=cuda"',
'# folder = Path("outputs/train/example_pusht_diffusion")',
'hub_id = "lerobot/diffusion_policy_pusht_image"',
"folder = Path(snapshot_download(hub_id)",
],
[
'"eval_episodes=1"',
'"rollout_batch_size=1"',
'"device=cpu"',
'folder = Path("outputs/train/example_pusht_diffusion")',
"",
"",
],
)
assert Path(f"outputs/train/example_pusht_diffusion").exists()
assert Path(f"outputs/train/example_pusht_diffusion").exists()