115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
import logging
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import os
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from pathlib import Path
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import torch
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from torchrl.data.replay_buffers import PrioritizedSliceSampler, SliceSampler
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from lerobot.common.envs.transforms import NormalizeTransform
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DATA_DIR = Path(os.environ.get("DATA_DIR", "data"))
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def make_offline_buffer(
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cfg, overwrite_sampler=None, normalize=True, overwrite_batch_size=None, overwrite_prefetch=None
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):
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if cfg.policy.balanced_sampling:
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assert cfg.online_steps > 0
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batch_size = None
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pin_memory = False
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prefetch = None
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else:
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assert cfg.online_steps == 0
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num_slices = cfg.policy.batch_size
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batch_size = cfg.policy.horizon * num_slices
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pin_memory = cfg.device == "cuda"
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prefetch = cfg.prefetch
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if overwrite_batch_size is not None:
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batch_size = overwrite_batch_size
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if overwrite_prefetch is not None:
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prefetch = overwrite_prefetch
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if overwrite_sampler is None:
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# TODO(rcadene): move batch_size outside
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num_traj_per_batch = cfg.policy.batch_size # // cfg.horizon
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# TODO(rcadene): Sampler outputs a batch_size <= cfg.batch_size.
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# We would need to add a transform to pad the tensordict to ensure batch_size == cfg.batch_size.
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if cfg.offline_prioritized_sampler:
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logging.info("use prioritized sampler for offline dataset")
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sampler = PrioritizedSliceSampler(
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max_capacity=100_000,
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alpha=cfg.policy.per_alpha,
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beta=cfg.policy.per_beta,
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num_slices=num_traj_per_batch,
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strict_length=False,
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)
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else:
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logging.info("use simple sampler for offline dataset")
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sampler = SliceSampler(
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num_slices=num_traj_per_batch,
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strict_length=False,
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)
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else:
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sampler = overwrite_sampler
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if cfg.env.name == "simxarm":
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from lerobot.common.datasets.simxarm import SimxarmExperienceReplay
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clsfunc = SimxarmExperienceReplay
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dataset_id = f"xarm_{cfg.env.task}_medium"
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elif cfg.env.name == "pusht":
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from lerobot.common.datasets.pusht import PushtExperienceReplay
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clsfunc = PushtExperienceReplay
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dataset_id = "pusht"
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elif cfg.env.name == "aloha":
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from lerobot.common.datasets.aloha import AlohaExperienceReplay
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clsfunc = AlohaExperienceReplay
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dataset_id = f"aloha_{cfg.env.task}"
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else:
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raise ValueError(cfg.env.name)
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offline_buffer = clsfunc(
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dataset_id=dataset_id,
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root=DATA_DIR,
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sampler=sampler,
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batch_size=batch_size,
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pin_memory=pin_memory,
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prefetch=prefetch if isinstance(prefetch, int) else None,
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)
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if normalize:
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# TODO(rcadene): make normalization strategy configurable between mean_std, min_max, manual_min_max, min_max_from_spec
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stats = offline_buffer.compute_or_load_stats()
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in_keys = [("observation", "state"), ("action")]
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if cfg.policy == "tdmpc":
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for key in offline_buffer.image_keys:
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# TODO(rcadene): imagenet normalization is applied inside diffusion policy, but no normalization inside tdmpc
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in_keys.append(key)
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# since we use next observations in tdmpc
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in_keys.append(("next", *key))
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in_keys.append(("next", "observation", "state"))
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if cfg.policy == "diffusion" and cfg.env.name == "pusht":
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# TODO(rcadene): we overwrite stats to have the same as pretrained model, but we should remove this
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stats["observation", "state", "min"] = torch.tensor([13.456424, 32.938293], dtype=torch.float32)
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stats["observation", "state", "max"] = torch.tensor([496.14618, 510.9579], dtype=torch.float32)
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stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
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stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
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transform = NormalizeTransform(stats, in_keys, mode="min_max")
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offline_buffer.set_transform(transform)
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if not overwrite_sampler:
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index = torch.arange(0, offline_buffer.num_samples, 1)
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sampler.extend(index)
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return offline_buffer
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