68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
from pathlib import Path
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def _find_and_replace(text: str, finds_and_replaces: list[tuple[str, str]]) -> str:
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for f, r in finds_and_replaces:
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assert f in text
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text = text.replace(f, r)
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return text
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def test_example_1():
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path = "examples/1_visualize_dataset.py"
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with open(path, "r") as file:
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file_contents = file.read()
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exec(file_contents)
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assert Path("outputs/visualize_dataset/example/episode_0.mp4").exists()
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def test_examples_3_and_2():
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"""
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Train a model with example 3, check the outputs.
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Evaluate the trained model with example 2, check the outputs.
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"""
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path = "examples/3_train_policy.py"
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with open(path, "r") as file:
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file_contents = file.read()
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# Do less steps and use CPU.
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file_contents = _find_and_replace(
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file_contents,
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[
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("offline_steps = 5000", "offline_steps = 1"),
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('device = torch.device("cuda")', 'device = torch.device("cpu")'),
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],
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)
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exec(file_contents)
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for file_name in ["model.pt", "stats.pth", "config.yaml"]:
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assert Path(f"outputs/train/example_pusht_diffusion/{file_name}").exists()
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path = "examples/2_evaluate_pretrained_policy.py"
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with open(path, "r") as file:
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file_contents = file.read()
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# Do less evals, use CPU, and use the local model.
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file_contents = _find_and_replace(
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file_contents,
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[
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('"eval_episodes=10"', '"eval_episodes=1"'),
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('"rollout_batch_size=10"', '"rollout_batch_size=1"'),
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('"device=cuda"', '"device=cpu"'),
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(
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'# folder = Path("outputs/train/example_pusht_diffusion")',
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'folder = Path("outputs/train/example_pusht_diffusion")',
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),
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('hub_id = "lerobot/diffusion_policy_pusht_image"', ""),
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("folder = Path(snapshot_download(hub_id)", ""),
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],
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)
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assert Path(f"outputs/train/example_pusht_diffusion").exists()
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