works
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@ -33,7 +33,7 @@ from lerobot.common.utils.utils import init_logging
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def check_format(raw_dir) -> bool:
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def check_format(raw_dir) -> bool:
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# TODO(rcadene): remove hardcoding
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# TODO(rcadene): remove hardcoding
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raw_dir = raw_dir / "018f9fdc-6b7b-7432-a529-40d2cc718032"
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raw_dir = raw_dir / "018fa076-ad19-7c77-afa4-49f7f072e86f"
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assert raw_dir.exists()
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assert raw_dir.exists()
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leader_file = list(raw_dir.glob("*.parquet"))
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leader_file = list(raw_dir.glob("*.parquet"))
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@ -44,7 +44,7 @@ def check_format(raw_dir) -> bool:
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def load_from_raw(raw_dir: Path, out_dir: Path):
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def load_from_raw(raw_dir: Path, out_dir: Path):
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# TODO(rcadene): remove hardcoding
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# TODO(rcadene): remove hardcoding
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raw_dir = raw_dir / "018f9fdc-6b7b-7432-a529-40d2cc718032"
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raw_dir = raw_dir / "018fa076-ad19-7c77-afa4-49f7f072e86f"
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# Load data stream that will be used as reference for the timestamps synchronization
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# Load data stream that will be used as reference for the timestamps synchronization
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reference_key = "observation.images.cam_right_wrist"
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reference_key = "observation.images.cam_right_wrist"
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@ -91,7 +91,7 @@ def load_from_raw(raw_dir: Path, out_dir: Path):
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# sanity check episode indices go from 0 to n-1
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# sanity check episode indices go from 0 to n-1
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ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
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ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
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expected_ep_ids = list(range(df["episode_index"].max()))
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expected_ep_ids = list(range(df["episode_index"].max() + 1))
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assert ep_ids == expected_ep_ids, f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}"
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assert ep_ids == expected_ep_ids, f"Episodes indices go from {ep_ids} instead of {expected_ep_ids}"
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# Create symlink to raw videos directory (that needs to be absolute not relative)
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# Create symlink to raw videos directory (that needs to be absolute not relative)
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@ -25,8 +25,8 @@ training:
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offline_steps: 80000
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offline_steps: 80000
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online_steps: 0
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online_steps: 0
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eval_freq: 99999999999999
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eval_freq: 99999999999999
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save_freq: 100000
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save_freq: 1000
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log_freq: 250
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log_freq: 100
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save_model: true
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save_model: true
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batch_size: 8
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batch_size: 8
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