Refactor datasets with abstract class
This commit is contained in:
Remi Cadene
@@ -1,6 +1,3 @@
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import logging
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import math
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import os
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from pathlib import Path
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from typing import Callable
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@@ -12,16 +9,14 @@ 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 Sampler
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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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from torchrl.data.replay_buffers.samplers import SliceSampler
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from torchrl.data.replay_buffers.storages import TensorStorage
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from torchrl.data.replay_buffers.writers import Writer
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from diffusion_policy.common.replay_buffer import ReplayBuffer as DiffusionPolicyReplayBuffer
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from diffusion_policy.env.pusht.pusht_env import pymunk_to_shapely
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from lerobot.common.datasets.abstract import AbstractExperienceReplay
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from lerobot.common.datasets.utils import download_and_extract_zip
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from lerobot.common.envs.transforms import NormalizeTransform
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# as define in env
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SUCCESS_THRESHOLD = 0.95 # 95% coverage,
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@@ -87,114 +82,36 @@ def add_tee(
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return body
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class PushtExperienceReplay(TensorDictReplayBuffer):
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class PushtExperienceReplay(AbstractExperienceReplay):
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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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num_slices: int = None,
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slice_len: int = None,
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pad: float = None,
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replacement: bool = None,
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streaming: bool = False,
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root: Path = None,
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sampler: Sampler = None,
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writer: Writer = None,
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collate_fn: Callable = None,
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pin_memory: bool = False,
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prefetch: int = None,
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transform: "torchrl.envs.Transform" = None, # noqa: F821
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split_trajs: bool = False,
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strict_length: bool = True,
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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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if streaming:
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raise NotImplementedError
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self.streaming = streaming
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self.dataset_id = dataset_id
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self.split_trajs = split_trajs
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self.shuffle = shuffle
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self.num_slices = num_slices
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self.slice_len = slice_len
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self.pad = pad
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self.strict_length = strict_length
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if (self.num_slices is not None) and (self.slice_len is not None):
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raise ValueError("num_slices or slice_len can be not None, but not both.")
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if split_trajs:
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raise NotImplementedError
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if root is None:
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root = _get_root_dir("pusht")
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os.makedirs(root, exist_ok=True)
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self.root = root
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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.root / dataset_id))
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stats = self._compute_or_load_stats(storage)
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transform = NormalizeTransform(
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stats,
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in_keys=[
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# TODO(rcadene): imagenet normalization is applied inside diffusion policy
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# We need to automate this for tdmpc and others
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# ("observation", "image"),
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("observation", "state"),
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# TODO(rcadene): for tdmpc, we might want next image and state
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# ("next", "observation", "image"),
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# ("next", "observation", "state"),
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("action"),
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],
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mode="min_max",
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)
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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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transform.stats["observation", "state", "min"] = torch.tensor(
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[13.456424, 32.938293], dtype=torch.float32
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)
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transform.stats["observation", "state", "max"] = torch.tensor(
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[496.14618, 510.9579], dtype=torch.float32
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)
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transform.stats["action", "min"] = torch.tensor([12.0, 25.0], dtype=torch.float32)
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transform.stats["action", "max"] = torch.tensor([511.0, 511.0], dtype=torch.float32)
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if writer is None:
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writer = ImmutableDatasetWriter()
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if collate_fn is None:
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collate_fn = _collate_id
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super().__init__(
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storage=storage,
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sampler=sampler,
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writer=writer,
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collate_fn=collate_fn,
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dataset_id,
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batch_size,
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shuffle=shuffle,
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root=root,
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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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sampler=sampler,
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collate_fn=collate_fn,
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writer=writer,
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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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@property
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def data_path_root(self) -> Path:
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return None if self.streaming else self.root / self.dataset_id
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def _is_downloaded(self) -> bool:
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return self.data_path_root.is_dir()
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def _download_and_preproc(self):
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# download
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raw_dir = self.root / "raw"
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raw_dir = self.data_dir / "raw"
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zarr_path = (raw_dir / PUSHT_ZARR).resolve()
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if not zarr_path.is_dir():
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raw_dir.mkdir(parents=True, exist_ok=True)
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@@ -286,7 +203,7 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
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if episode_id == 0:
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# hack to initialize tensordict data structure to store episodes
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td_data = episode[0].expand(total_frames).memmap_like(self.root / self.dataset_id)
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td_data = episode[0].expand(total_frames).memmap_like(self.data_dir)
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td_data[idxtd : idxtd + len(episode)] = episode
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@@ -294,112 +211,3 @@ class PushtExperienceReplay(TensorDictReplayBuffer):
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idxtd = idxtd + len(episode)
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return TensorStorage(td_data.lock_())
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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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def _compute_or_load_stats(self, storage) -> TensorDict:
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stats_path = self.root / self.dataset_id / "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(storage)
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torch.save(stats, stats_path)
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return stats
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