56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import torch
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from omegaconf import OmegaConf
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from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
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def make_dataset(
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cfg,
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split="train",
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):
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if cfg.env.name not in cfg.dataset_repo_id:
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logging.warning(
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f"There might be a mismatch between your training dataset ({cfg.dataset_repo_id=}) and your "
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f"environment ({cfg.env.name=})."
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)
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delta_timestamps = cfg.training.get("delta_timestamps")
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if delta_timestamps is not None:
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for key in delta_timestamps:
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if isinstance(delta_timestamps[key], str):
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delta_timestamps[key] = eval(delta_timestamps[key])
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# TODO(rcadene): add data augmentations
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dataset = LeRobotDataset(
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cfg.dataset_repo_id,
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split=split,
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delta_timestamps=delta_timestamps,
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)
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if cfg.get("override_dataset_stats"):
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for key, stats_dict in cfg.override_dataset_stats.items():
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for stats_type, listconfig in stats_dict.items():
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# example of stats_type: min, max, mean, std
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stats = OmegaConf.to_container(listconfig, resolve=True)
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dataset.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
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return dataset
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