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Merge branch 'main' into fix/lint_warnings

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Steven Palma 2025-03-07 18:07:30 +01:00 committed by GitHub
parent f9e4a1f5c4 074f0ac8fe
commit b104be0d04
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2 changed files with 5 additions and 4 deletions
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2
examples/2_evaluate_pretrained_policy.py
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@ -44,7 +44,7 @@ pretrained_policy_path = "lerobot/diffusion_pusht"
# OR a path to a local outputs/train folder. # OR a path to a local outputs/train folder.
# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion") # pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")
policy = DiffusionPolicy.from_pretrained(pretrained_policy_path, map_location=device) policy = DiffusionPolicy.from_pretrained(pretrained_policy_path)
# Initialize evaluation environment to render two observation types: # Initialize evaluation environment to render two observation types:
# an image of the scene and state/position of the agent. The environment # an image of the scene and state/position of the agent. The environment

7
tests/test_policies.py
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@ -252,11 +252,12 @@ def test_save_and_load_pretrained(dummy_dataset_metadata, tmp_path, policy_name:
policy_cfg.input_features = { policy_cfg.input_features = {
key: ft for key, ft in features.items() if key not in policy_cfg.output_features key: ft for key, ft in features.items() if key not in policy_cfg.output_features
} }
policy = policy_cls(policy_cfg) # config.device = gpu policy = policy_cls(policy_cfg)
policy.to(policy_cfg.device)
save_dir = tmp_path / f"test_save_and_load_pretrained_{policy_cls.__name__}" save_dir = tmp_path / f"test_save_and_load_pretrained_{policy_cls.__name__}"
policy.save_pretrained(save_dir) policy.save_pretrained(save_dir)
policy_ = policy_cls.from_pretrained(save_dir, config=policy_cfg) loaded_policy = policy_cls.from_pretrained(save_dir, config=policy_cfg)
assert all(torch.equal(p, p_) for p, p_ in zip(policy.parameters(), policy_.parameters(), strict=True)) torch.testing.assert_close(list(policy.parameters()), list(loaded_policy.parameters()), rtol=0, atol=0)
@pytest.mark.parametrize("insert_temporal_dim", [False, True]) @pytest.mark.parametrize("insert_temporal_dim", [False, True])