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lerobot
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Merge remote-tracking branch 'upstream/main' into remove_extra_frame

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Alexander Soare 2024-05-05 12:07:35 +01:00
parent c00e8dab81 f6fe162432
commit a5f85c71a5
4 changed files with 7 additions and 7 deletions
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2
README.md
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@ -135,7 +135,7 @@ Check out [examples](./examples) to see how you can load a pretrained policy fro
Or you can achieve the same result by executing our script from the command line: Or you can achieve the same result by executing our script from the command line:
```bash ```bash
python lerobot/scripts/eval.py \ python lerobot/scripts/eval.py \
-p lerobot/diffusion_policy_pusht_image \ -p lerobot/diffusion_pusht \
eval_episodes=10 \ eval_episodes=10 \
hydra.run.dir=outputs/eval/example_hub hydra.run.dir=outputs/eval/example_hub
``` ```

2
examples/2_evaluate_pretrained_policy.py
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@ -10,7 +10,7 @@ from huggingface_hub import snapshot_download
from lerobot.scripts.eval import eval from lerobot.scripts.eval import eval
# Get a pretrained policy from the hub. # Get a pretrained policy from the hub.
pretrained_policy_name = "lerobot/diffusion_policy_pusht_image" pretrained_policy_name = "lerobot/diffusion_pusht"
pretrained_policy_path = Path(snapshot_download(pretrained_policy_name)) pretrained_policy_path = Path(snapshot_download(pretrained_policy_name))
# OR uncomment the following to evaluate a policy from the local outputs/train folder. # OR uncomment the following to evaluate a policy from the local outputs/train folder.
# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion") # pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")

8
lerobot/scripts/eval.py
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@ -2,18 +2,18 @@
Usage examples: Usage examples:
You want to evaluate a model from the hub (eg: https://huggingface.co/lerobot/diffusion_policy_pusht_image) You want to evaluate a model from the hub (eg: https://huggingface.co/lerobot/diffusion_pusht)
for 10 episodes. for 10 episodes.
``` ```
python lerobot/scripts/eval.py -p lerobot/diffusion_policy_pusht_image eval.n_episodes=10 python lerobot/scripts/eval.py -p lerobot/diffusion_pusht eval.n_episodes=10
``` ```
OR, you want to evaluate a model checkpoint from the LeRobot training script for 10 episodes. OR, you want to evaluate a model checkpoint from the LeRobot training script for 10 episodes.
``` ```
python lerobot/scripts/eval.py \ python lerobot/scripts/eval.py \
-p outputs/train/diffusion_policy_pusht_image/checkpoints/005000 \ -p outputs/train/diffusion_pusht/checkpoints/005000 \
eval.n_episodes=10 eval.n_episodes=10
``` ```
@ -23,7 +23,7 @@ Note that in both examples, the repo/folder should contain at least `config.json
Note the formatting for providing the number of episodes. Generally, you may provide any number of arguments Note the formatting for providing the number of episodes. Generally, you may provide any number of arguments
with `qualified.parameter.name=value`. In this case, the parameter eval.n_episodes appears as `n_episodes` with `qualified.parameter.name=value`. In this case, the parameter eval.n_episodes appears as `n_episodes`
nested under `eval` in the `config.yaml` found at nested under `eval` in the `config.yaml` found at
https://huggingface.co/lerobot/diffusion_policy_pusht_image/tree/main. https://huggingface.co/lerobot/diffusion_pusht/tree/main.
""" """
import argparse import argparse

2
tests/test_examples.py
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@ -58,7 +58,7 @@ def test_examples_3_and_2():
file_contents = _find_and_replace( file_contents = _find_and_replace(
file_contents, file_contents,
[ [
('pretrained_policy_name = "lerobot/diffusion_policy_pusht_image"', ""), ('pretrained_policy_name = "lerobot/diffusion_pusht"', ""),
("pretrained_policy_path = Path(snapshot_download(pretrained_policy_name))", ""), ("pretrained_policy_path = Path(snapshot_download(pretrained_policy_name))", ""),
( (
'# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")', '# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',