Refactor datasets with abstract class
This commit is contained in:
Remi Cadene
185
lerobot/common/datasets/abstract.py
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185
lerobot/common/datasets/abstract.py
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import abc
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import logging
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import math
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from pathlib import Path
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from typing import Callable
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import einops
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import torch
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import torchrl
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import tqdm
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from tensordict import TensorDict
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from torchrl.data.datasets.utils import _get_root_dir
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from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
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from torchrl.data.replay_buffers.samplers import SliceSampler
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from torchrl.data.replay_buffers.storages import TensorStorage, _collate_id
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from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
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class AbstractExperienceReplay(TensorDictReplayBuffer):
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def __init__(
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self,
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dataset_id: str,
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batch_size: int = None,
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*,
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shuffle: bool = True,
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root: Path = None,
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pin_memory: bool = False,
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prefetch: int = None,
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sampler: SliceSampler = None,
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collate_fn: Callable = None,
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writer: Writer = None,
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transform: "torchrl.envs.Transform" = None, # noqa-F821
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):
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self.dataset_id = dataset_id
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self.shuffle = shuffle
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self.root = _get_root_dir(self.dataset_id) if root is None else root
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self.root = Path(self.root)
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self.data_dir = self.root / self.dataset_id
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storage = self._download_or_load_storage()
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super().__init__(
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storage=storage,
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sampler=sampler,
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writer=ImmutableDatasetWriter() if writer is None else writer,
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collate_fn=_collate_id if collate_fn is None else collate_fn,
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pin_memory=pin_memory,
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prefetch=prefetch,
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batch_size=batch_size,
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transform=transform,
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)
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@property
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def num_samples(self) -> int:
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return len(self)
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@property
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def num_episodes(self) -> int:
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return len(self._storage._storage["episode"].unique())
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def set_transform(self, transform):
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self.transform = transform
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def compute_or_load_stats(self, num_batch=100, batch_size=32) -> TensorDict:
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stats_path = self.data_dir / "stats.pth"
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if stats_path.exists():
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stats = torch.load(stats_path)
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else:
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logging.info(f"compute_stats and save to {stats_path}")
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stats = self._compute_stats(self._storage._storage, num_batch, batch_size)
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torch.save(stats, stats_path)
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return stats
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@abc.abstractmethod
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def _download_and_preproc(self) -> torch.StorageBase:
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raise NotImplementedError()
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def _download_or_load_storage(self):
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if not self._is_downloaded():
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storage = self._download_and_preproc()
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else:
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storage = TensorStorage(TensorDict.load_memmap(self.data_dir))
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return storage
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def _is_downloaded(self) -> bool:
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return self.data_dir.is_dir()
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def _compute_stats(self, storage, num_batch=100, batch_size=32):
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rb = TensorDictReplayBuffer(
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storage=storage,
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batch_size=batch_size,
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prefetch=True,
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)
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batch = rb.sample()
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image_channels = batch["observation", "image"].shape[1]
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image_mean = torch.zeros(image_channels)
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image_std = torch.zeros(image_channels)
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image_max = torch.tensor([-math.inf] * image_channels)
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image_min = torch.tensor([math.inf] * image_channels)
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state_channels = batch["observation", "state"].shape[1]
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state_mean = torch.zeros(state_channels)
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state_std = torch.zeros(state_channels)
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state_max = torch.tensor([-math.inf] * state_channels)
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state_min = torch.tensor([math.inf] * state_channels)
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action_channels = batch["action"].shape[1]
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action_mean = torch.zeros(action_channels)
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action_std = torch.zeros(action_channels)
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action_max = torch.tensor([-math.inf] * action_channels)
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action_min = torch.tensor([math.inf] * action_channels)
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for _ in tqdm.tqdm(range(num_batch)):
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image_mean += einops.reduce(batch["observation", "image"], "b c h w -> c", "mean")
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state_mean += einops.reduce(batch["observation", "state"], "b c -> c", "mean")
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action_mean += einops.reduce(batch["action"], "b c -> c", "mean")
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b_image_max = einops.reduce(batch["observation", "image"], "b c h w -> c", "max")
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b_image_min = einops.reduce(batch["observation", "image"], "b c h w -> c", "min")
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b_state_max = einops.reduce(batch["observation", "state"], "b c -> c", "max")
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b_state_min = einops.reduce(batch["observation", "state"], "b c -> c", "min")
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b_action_max = einops.reduce(batch["action"], "b c -> c", "max")
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b_action_min = einops.reduce(batch["action"], "b c -> c", "min")
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image_max = torch.maximum(image_max, b_image_max)
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image_min = torch.maximum(image_min, b_image_min)
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state_max = torch.maximum(state_max, b_state_max)
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state_min = torch.maximum(state_min, b_state_min)
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action_max = torch.maximum(action_max, b_action_max)
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action_min = torch.maximum(action_min, b_action_min)
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batch = rb.sample()
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image_mean /= num_batch
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state_mean /= num_batch
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action_mean /= num_batch
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for i in tqdm.tqdm(range(num_batch)):
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b_image_mean = einops.reduce(batch["observation", "image"], "b c h w -> c", "mean")
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b_state_mean = einops.reduce(batch["observation", "state"], "b c -> c", "mean")
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b_action_mean = einops.reduce(batch["action"], "b c -> c", "mean")
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image_std += (b_image_mean - image_mean) ** 2
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state_std += (b_state_mean - state_mean) ** 2
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action_std += (b_action_mean - action_mean) ** 2
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b_image_max = einops.reduce(batch["observation", "image"], "b c h w -> c", "max")
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b_image_min = einops.reduce(batch["observation", "image"], "b c h w -> c", "min")
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b_state_max = einops.reduce(batch["observation", "state"], "b c -> c", "max")
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b_state_min = einops.reduce(batch["observation", "state"], "b c -> c", "min")
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b_action_max = einops.reduce(batch["action"], "b c -> c", "max")
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b_action_min = einops.reduce(batch["action"], "b c -> c", "min")
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image_max = torch.maximum(image_max, b_image_max)
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image_min = torch.maximum(image_min, b_image_min)
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state_max = torch.maximum(state_max, b_state_max)
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state_min = torch.maximum(state_min, b_state_min)
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action_max = torch.maximum(action_max, b_action_max)
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action_min = torch.maximum(action_min, b_action_min)
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if i < num_batch - 1:
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batch = rb.sample()
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image_std = torch.sqrt(image_std / num_batch)
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state_std = torch.sqrt(state_std / num_batch)
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action_std = torch.sqrt(action_std / num_batch)
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stats = TensorDict(
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{
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("observation", "image", "mean"): image_mean[None, :, None, None],
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("observation", "image", "std"): image_std[None, :, None, None],
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("observation", "image", "max"): image_max[None, :, None, None],
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("observation", "image", "min"): image_min[None, :, None, None],
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("observation", "state", "mean"): state_mean[None, :],
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("observation", "state", "std"): state_std[None, :],
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("observation", "state", "max"): state_max[None, :],
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("observation", "state", "min"): state_min[None, :],
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("action", "mean"): action_mean[None, :],
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("action", "std"): action_std[None, :],
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("action", "max"): action_max[None, :],
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("action", "min"): action_min[None, :],
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},
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batch_size=[],
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)
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stats["next", "observation", "image"] = stats["observation", "image"]
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stats["next", "observation", "state"] = stats["observation", "state"]
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return stats
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